Quality management and intelligent manufacturing with labels and smart tags in event-based product manufacturing

ABSTRACT

Providing quality management and intelligent manufacturing with labels and smart tags in event-based product manufacturing. Some of the disclosed embodiments include a system, method, and computer-readable media for storing, during a process, data associated with a material. Also disclosed are a method of collecting, storing, and reporting machine productivity, waste, and delay information on an event basis in a manufacturing system, a method of capturing and storing material history, a method of automating tracking of positions of components used in a process and correlating portions of a component with production problems, an improved inventory management system, and a method of tracking and recording actions of specific operators of a process performed by a machine. The embodiments are operable in an intelligent manufacturing system including a process for converting raw materials to a product, a process control system including one or more sensors capable of generating an alarm in response to an event that results in one of waste, machine delay, or decrease product quality, a data logger associated with the process control system for obtaining event parameters associated with the event, a database on a server for recording event parameters obtained by the data logger, and a reporting system cooperatively associated with the database for reporting productivity parameters regarding the process derived at least in part from the event parameters.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/344,747, filed Dec. 28, 2001, herein incorporated by reference.

TECHNICAL FIELD

The present invention relates to the field of product manufacturing. Inparticular, this invention relates to quality management and intelligentmanufacturing with labels and smart tags in event-based productmanufacturing.

BACKGROUND

In manufacturing, the realized capacity of a machine or productionfacility may be substantially less than the theoretically maximumcapacity for any number of reasons, including machine stoppage or delaycaused by quality problems, machine failure, inadequate manpower,unavailable raw materials, and the like. Many attempts have been made toimprove statistical process control of machines and plants to improvequality, and other efforts have been made to optimize machinemaintenance, raw materials purchasing, inventory management and so forthto generally increase productivity.

Previous efforts have failed to adequately document and analyze the manyfactors that may be associated with machine delay or other productivityproblems. Further, a manufacturing information system does not yetappear to have been developed which can directly provide accounting datafor financial reports based on data obtained directly from amanufacturing site pertaining to process events and associatedproductivity parameters.

Thus, there is a need to provide an improved manufacturing informationsystem for tracking and analyzing causes of delay and waste inmanufacturing. There is also a need to integrate the improvedmanufacturing information system with financial reporting means to allowaccountants, management, and others to readily obtain financialinformation regarding one or more machines or plants.

Further, in the production of goods from raw materials and intermediatecomponents, it is an ongoing challenge to ensure that proper rawmaterials are used, and to track the effect of the raw materials on theproductivity of a machine. Improved systems are needed for handling andtracking raw materials to improve the productivity of a process. Forexample, using present systems, it is often possible for an incorrectraw material to be loaded into a process, and that process may continueto operate for hours, yielding product that does not comply withspecifications, sometimes resulting in enormous waste. There is a needfor improved automated systems to prevent such waste and validate rawmaterials used in a process.

For these reasons, an event-based manufacturing information system isdesired to address one or more of these and other disadvantages.

SUMMARY

The invention is operable in an intelligent manufacturing systemincluding a process for converting raw materials to a product, a processcontrol system including one or more sensors capable of generating analarm in response to an event that results in one of waste, machinedelay, or decrease product quality, a data logger associated with theprocess control system for obtaining event parameters associated withthe event, a database on a server for recording event parametersobtained by the data logger, and a reporting system cooperativelyassociated with the database for reporting productivity parametersregarding the process derived at least in part from the eventparameters.

Briefly, a system stores, during a process, data associated with amaterial. The system includes a control system for collecting, during afirst process, event data relating to a material. The event dataincludes an event code and a value pertaining to an attribute orphysical property of the material affected by the event. The system alsoincludes a memory device for storing the collected event data as a datarecord. An identifier within the memory device is associated with thedata record. The data record is accessible via its associated identifierso that the collected event data in the memory device is obtainableduring a second process occurring subsequent to the first process. Thesecond process is adapted to be modified responsive to the event data.

In one aspect, a method stores data associated with a material. Themethod includes collecting, during a first process, event data relatingto a material. The method also includes storing the collected event dataas a data record. The event data includes information indicating thelocation within the material where a quality defect may occur. Anidentifier is associated with the data record. The data record isaccessible via its associated identifier so that the collected eventdata is obtainable during a second process occurring subsequent to thefirst process. The second process is adapted to be modified responsiveto the event data to reduce the impact on the process of a qualitydefect in the material.

In another aspect, one or more computer-readable media havecomputer-executable components including a control module and a databasemodule. The control module collects, during a first process, event datarelating to a material. The database module stores the event datacollected by the control module as a data record. An identifier withinthe database module is associated with the data record. The data recordis accessible via its associated identifier so that the collected eventdata in the database module is obtainable during a second processoccurring subsequent to the first process.

In yet another aspect, a method collects, stores, and reports machineproductivity, waste, and delay information on an event basis in amanufacturing system. The method includes monitoring an event via aprocess sensor. The method also includes detecting an event trigger inresponse to the monitoring. The method also includes obtaining data inresponse to the detecting. The method also includes a process variablefrom a control system, a measure of the waste, delay, or quality lossassociated with the event, and operator input. The method also includesautomatically validating the obtained data. The method also includesformatting and recording the validated data. The method also includesgenerating a report based on the recorded data.

In still another aspect, a method in an event-based manufacturing systemincludes receiving a vendor identifier from a manufacturer. The methodalso includes receiving an order for a material from the manufacturer,producing the material, and creating a batch code for the producedmaterial. The method also includes measuring a material property of theproduced material and storing the measured material property as materialproperty data in a material property database for access by themanufacturer. The method also includes applying a label including anidentifier to the produced material. The identifier includes the vendoridentifier and the created batch code. The method also includes shippingthe produced material and its applied label to the manufacturer. Themethod also includes producing a product using the produced material asa raw material in a process having a control system configured to recordwaste and delay events as event data in an event database comprisingevent records. The method also includes correlating event data to thematerial property data in the material property database.

In another aspect, one or more computer-readable media store a datastructure representing an identifier for a material in an event-basedmanufacturing system. The data structure includes a first field storinga vendor code representing a vendor of the material. The data structurealso includes a second field storing a batch code assigned by the vendorrepresenting a batch of the material.

In still another aspect, in an event-based manufacturing system, one ormore computer-readable media for use in conjunction with a secondprocess occurring after a first process, store a data structurerepresenting event data for a material. The data structure includes oneor more fields storing data describing characteristics of the materialand one or more codes describing the nature of an event. The datastructure is populated during the first process and is accessible duringthe second process.

In yet another aspect, a method captures and stores material history inan event-based manufacturing system. The method includes implementing acommon database format among a plurality of vendors. The method alsoincludes receiving data in the common database format from each of theplurality of vendors. The data represents event data collected for amaterial during a process. The method also includes storing the receiveddata in a database for access during a subsequent process. The secondprocess is adapted to be automatically modified responsive the receiveddata.

In another aspect, a method automates tracking of positions ofcomponents used in a process and correlates portions of a component withproduction problems. The method includes embedding a plurality ofidentification devices in a material. The method also includesmonitoring the plurality of identification devices as the materialpasses through a component to obtain material position data indicating aposition of the material with respect to the component. The method alsoincludes storing the material position data in a database including oroperatively associated with event-based data for the process. The methodalso includes correlating the stored material position data with aquality control issue to identify corrective action.

In still another aspect, an improved inventory management system in anevent-based manufacturing system includes monitoring at least oneidentification device associated with an inventory item from a firstprocess. The system also includes determining a physical location of theinventory item in response to the monitoring to use the inventory itemin a second process. The system also includes associating the physicallocation of the inventory item with event-based data from themanufacture of the inventory item pertaining to the quality of theinventory item.

In yet another aspect, in a distributed control system for event-basedmanufacturing, a method tracks and records actions of specific operatorsof a process performed by a machine. The system includes reading, from aplurality of scanning devices, an identification device identifying anoperator. The system also includes verifying an identity of theoperator. The system also includes tracking a time and place of theoperator relative to the process via the reading and verifying.

Alternatively, the invention may comprise various other methods andapparatuses.

Other features will be in part apparent and in part pointed outhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram illustrating a manufacturingprocess for a product that includes a first process producing anintermediate product from a raw material and a second process producinga final product from the intermediate product.

FIG. 2 is an exemplary block diagram illustrating a manufacturingprocess for a product with a bill of materials explicitly shown.

FIG. 3 is an exemplary flow chart showing several of the steps involvedin a system according to the invention used for financial reporting.

FIG. 4 is an exemplary graph illustrating one definition of delay duringa series of events relating to machine productivity over time.

FIG. 5 is an exemplary block diagram illustrating ways in which therealized capacity (productivity or production rate) of a machine may beless than the maximum capacity.

FIG. 6 is an exemplary block diagram depicting an embodiment of acommercial operation according to the invention in which raw materialsare converted by a process to yield a product.

FIG. 7 is an exemplary block diagram depicting another embodiment of acommercial operation according to the invention in which raw materialsare converted by a process to yield a product

FIG. 8 is an exemplary flow chart showing steps preceding the shipmentof a product to a manufacturer, in association with the manufacturer'suse of a system according to the invention.

FIG. 9 is an exemplary block diagram depicting one embodiment of ahardware configuration according to the invention.

FIG. 10A and FIG. 10B are exemplary block diagrams showing how theproduct variables and process variables may be loaded, saved, andupdated for a manufacturing process incorporating a system of thepresent invention.

FIG. 11 is an exemplary flow chart illustrating audit operations formodifying data from a database according to the invention.

FIG. 12 is an exemplary flow chart illustrating audit operations fordeleting data from a database according to the invention.

FIG. 13 is a screen shot of the user interface for one exemplaryembodiment of the invention illustrating a menu allowing selections forreport generation.

FIG. 14 is an exemplary graph illustrating normalized probability ofmachine stop over time.

FIG. 15 is an exemplary bar chart illustrating the yield in a plant overa ten-week period, including weekly averages, a moving three-weekaverage, and an average from the previous quarter.

FIG. 16 is an exemplary bar chart illustrating the uptime in a plantover a ten-week period, including weekly averages, a moving three-weekaverage, and an average from the previous quarter.

FIG. 17 is an exemplary bar chart illustrating opportunity costs for asingle delay event on a single machine including weekly results over tenweeks and a three-week moving average.

FIG. 18 is an exemplary bar chart illustrating the top six most costlycauses of opportunity loss for a specified time period on a particularmachine.

Corresponding reference characters indicate corresponding partsthroughout the drawings.

DETAILED DESCRIPTION

An intelligent manufacturing system for tracking production informationfrom one or more manufacturing facilities has been developed. The systemis known as PIPE (Process Information Per Event). PIPE collects, stores,and reports production information such as converting machineproductivity, waste, and delay information on an event basis. In thissystem, machine data from sensors and other control means arecontinually monitored for events related to productivity and/or productquality, such as product waste, machine down time, machine slow downs,product maintenance, machine failure, etc. Customized rules may beestablished to specify how events are classified and what types ofevents are to be logged (normally, all sources of delay may be loggedand coupled with additional data). These events may be spaced apart intime by time steps that typically are not constant, and may besubstantially randomly spaced in time, or may be characterized in thatthe standard deviation of the time step between successive events islarge relative to the mean, such that the ratio of the standarddeviation to the mean time step during a week of production is about 0.2or greater, specifically about 0.5 or greater, and most specificallyabout 1.0 or greater. Time steps between successive events may range,for example, from a few seconds or minutes to hours or days, dependingon the process.

An “event,” as used herein, refers to any incident that may affect theproductivity of a process or machine in use to produce a product, orthat may adversely affect the quality of the product being produced.Events that adversely affect the productivity of a process or machine byincreasing delay are “adverse productivity events.” Productivity eventsthat lead to waste are “waste events,” while those that cause delay are“delay events.” Events that adversely affect the quality of a productare “adverse quality events.” As used herein, “intermediate events” mayrefer to incidents during a first process for the production of anintermediate product to be used as a raw material (starting material) ina second process for the production of a finished product (or anotherintermediate product or product component), wherein the incident in thefirst process may affect the productivity of the second process oradversely affect the quality of the product of the second process. Thus,an intermediate event in a first process may lead to an adverseproductivity event or an adverse quality event in a second process. Anadverse quality event may also refer to incidents that may adverselyaffect the quality of an intermediate product, such that the risk ofrejection of the product by a subsequent user (including an industrialuser) is increased. The PIPE system may be used to track any or alltypes of events, including events from multiple machines and processeswherein intermediate products from early processes or machines are usedas raw materials in later processes or machines, and optionally whereinthe event data for the intermediate products are used by operators orprocess control equipment to properly execute the subsequent processesbased on the events associated with the intermediate product or, ingeneral, with the quality and property attributes of the intermediateproduct as recorded at least in part with a system including PIPE.

Examples of events may include a web break, a component failure in amachine, a loss of manpower (e.g., inadequate employees present during ashift), a loss of power, a fire, machine shutdown to change a grade(“changeover”) or perform routine maintenance, unacceptable quality inraw materials, market curtailment (e.g., inadequate orders or excessinventory), an experimental run, a startup, and the like.

As used herein, “production information” includes waste data, delaydata, and any other data related to production. In some systems,production information is segregated from waste and delay data, eventhough waste and delay data are considered production information. Ingeneral, the invention is operable with any form of waste data, delaydata, or other production information or event data. For example,production information includes raw material usage information. Rawmaterial usage information includes, but is not limited to, a rawmaterial lot number, an amount of material in a roll, a time the rollwas spliced on or off, a supplier of the material, a number of productsproduced from the roll of material, and a date the material wasproduced.

PIPE event data obtained during production are stored in a databaseassociated with descriptor information. This information may be used togenerate financial reports automatically for use by an accountingdepartment, a plant manager, financial officers, or others, or for usein an internal or public publication such as a report or web page. ThePIPE information may be rolled up from multiple machines, plants,sectors, and so forth, including a corporate-wide roll-up of PIPE data,to provide roll-up productivity measures.

The data from the machine are monitored and logged by a PIPE EventLogger, which may include an event logger and a machine logger. Theevent logger may also serve many functions in addition to receiving andprocessing data, such as ensuring that the raw materials fit thespecifications for the product to be made (in cooperation with aseparate raw materials tracking system described hereafter), or linkingoperating data to the PIPE database, or ensuring that adequateexplanations have been entered by operators to explain delay states thatoccurred on the machine. The machine logger provides an interface foroperators to provide explanations about delay states or product waste,but generally does not collect data from sensors or productionequipment. The event logger and the machine logger may be separateprograms or be part of a single program, or functions of both may beshared or split between multiple programs and servers.

The system may be structured to support multiple converting lines inmultiple plants as an enterprise information system. According to thepresent invention, a plant information system or enterprise informationsystem may be adapted to allow corporate financial and purchasingsystems to receive information from the PIPE system for direct use.Production reporting systems may be directly linked to multiple PIPEdata streams to provide rolled-up financial information or informationfor a single asset. The PIPE system and its accounting module may beinterfaced with or cooperatively associated with accounting softwaresuch as SAP brand software and SAP/R3, process control software such asWONDERWARE brand manufacturing and process control operator-machineinterface software (Wonderware Corp., Irvine, Calif.), neural networks,expert systems, fuzzy logic systems, and many other suitable softwaresystems. Further, the PIPE system may automatically submit work requestsand purchase orders to deal with causes of delays (particularlyequipment failure) as they are encountered. Further, the PIPE system maybe used to mine process and quality data to identify means to improveproductivity or quality.

Data from the PIPE system may also be integrated with other softwaresystems for financial tracking, production management and planning,supply chain management, inventory control, maintenance and reliabilityengineering, customer relationship management (CRM), and the like. Forexample, PIPE data may be included in the sources of information treatedby POWERFACTORE software from KPMG Consulting (McLean, Va.). With thisapproach, the relationship between system maintenance schedules andproduct quality may also be explored to optimize operations to improvefinancial returns.

PIPE data may also be integrated with data warehousing systems such asthe SAS INTELLIGENT WAREHOUSING SOLUTION marketed by the SAS Institute,Inc. (Cary, N.C.) and the KALIDO brand computer database managementprograms by Kalido, Inc. (Houston, Tex.) such as the Dynamic InformationWarehouse. Likewise, SAS/INTRNET brand computer software and SAS onlineanalytical processing (OLAP) technology from the SAS Institute, Inc. maybe combined with the PIPE system. Other exemplary OLAP tools include theESSBASE DB2 OLAP software from Hyperion Solutions Corporation(Sunnyvale, Calif.) and COGNOS POWERPLAY of Cognos Incorporated (Ottawa,Canada). General principles on the combination of OLAP with datawarehousing are disclosed by Surajit Chaudhuri and Umeshwar Dayal, “AnOverview of Data Warehousing and OLAP Technology,” ACM Sigmod Record,March 1997.

Other data warehousing and maintenance methods may be applied. By way ofexample, principles of data warehousing and warehousing technology aredisclosed in U.S. Pat. No. 6,418,450, “Data Warehouse ProgramsArchitecture,” issued Jul. 9, 2002 to Daudenarde; U.S. Pat. No.6,353,835, “Technique for Effectively Maintaining Materialized Views InA Data Warehouse,” issued Mar. 5, 2002 to Lieuwen; U.S. Pat. No.6,178,418, “Distributed Data Warehouse Query and Resource ManagementSystem,” issued Jan. 23, 2001 to Singer; U.S. Pat. No. 6,138,121,“Network Management Event Storage and Manipulation Using RelationalDatabase Technology in a Data Warehouse,” issued Oct. 24, 2000 to Costaet al.; and U.S. Pat. No. 5,781,911, “Integrated System and Method ofData Warehousing and Delivery,” issued Jul. 14, 1998 to Young et al.Historical, summarized, and consolidated data are typically present indata warehouses, which may be queried to guide decision making and thedevelopment of business plans, or to prepare summary financial reports.

In addition, the PIPE system may be combined with NET PROPLAN and othermanufacturing execution systems (MES) software systems and modules inthe NET COLLECTION by Network Systems International, Inc. (Greensboro,N.C.). For example, the PIPE data may be integrated with the NETSCHEDULER module and the NET EVENT TRACKER system. In addition, the PIPEsystem may be integrated or modified to communicate with the FOLDERSsystem and the FACTELLIGENCE brand software for assisting manufacturingoperations by CIMNET (Robesonia, Pa.).

Enterprise Resource Planning (ERP) systems may be coupled with PIPEsystems. Exemplary ERP systems include those marketed by suppliers suchas SAP (Newtown Square, Pa.), J D Edwards (Denver, Colo.), Manugistics(Rockville, Md.), Siebel Systems (San Mateo, Calif.), ROI Systems(Minneapolis, Minn.) including the MANAGE 2000 brand pre-recordedcomputer programs, or custom built systems. An exemplary tool forintegrating PIPE data and other data with ERP systems (SAP R/3 systemsin particular) and generating financial reports is DATA INTEGRATOR ofBusiness Objects Americas, Inc. (San Jose, Calif.).

Existing software and known methods may be used to determine thefinancial costs of waste and delays. A computer system for determiningthe financial cost of various production problems and processbottlenecks is disclosed by Van Der Vegt and Thompson in U.S. Pat. No.6,144,893, issued Nov. 7, 2000, and in U.S. Pat. No. 6,128,540, issuedOct. 3, 2000, both of which are herein incorporated by reference to theextent they are non-contradictory herewith. Columns 1 to 12 in U.S. Pat.No. 6,144,893 disclose the computer method, and columns 12 to 19 thereindisclose a method for generating a problem priority table for problemsin the process. The determination of the cost of a process problem maybe calculated based on whether the process is constrained by productionlimitations or whether the process is sales constrained (demand for theproduct is less than the maximum capacity of the machine).

Integrated systems, in which PIPE and other systems tie into purchasingand financial systems, may be used for many purposes. For example,information about a machine failure detected by PIPE may be used toautomatically order a failed part with an asset management processutilizing SAP or other systems. Production tracked with PIPE may becombined with financial reporting tools, components, or modules as well.Neural network/fuzzy logic analysis of PIPE and related data, includingraw material data that is linked to the PIPE system via a raw materialtracking system, may be used to optimize profitability and improveprocess control, identify weaknesses in systems, parts, or vendorperformance, and so forth. Results may be displayed on a web page tolocal or remote viewers (typically authorized viewers only); displayedvia a client (e.g., through a window on a monitor for a Human-MachineInterface such as WONDERWARE brand manufacturing and process controloperator-machine interface software); incorporated into weekly, monthly,and annual reports; used to guide daily operations; and so forth. Timeseries of productivity parameters, such as measures of waste or delaymay be displayed graphically to show trends or ranges, in tabulatedform, including means and for various periods of time, and so forth.Productivity results may be sorted and/or displayed according to sector,machine type, product classification, geographical location, technologyor raw material types used in production (to examine the effect of achange in a production technology or raw material implemented at one ormore plants), and the like. In generating reports, any suitable type ofchart or graph may be used, and results may be put into any suitablesoftware format.

PIPE may be adapted to provide information for key performanceindicators (KPIs) expressed in terms of common performance measures,wherein a standardized definition and formula based on PIPE data isapplied. For example, one KPI may be percent total waste, expressed asthe ratio of the total number of products discarded to the totalproducts made. KPIs are identified by financial departments to describeprofitability, efficiency, production rates, etc., for individualmachines, plants, groups of plants, product categories, and so forth.Another KPI may be system rate, which is the actual machine speeddivided by target speed, commonly expressed as a percentage. Actualspeed may be defined as total standard units produced divided by actualhours of operation. Waste may be calculated as total units producedminus acceptable units products, or may be expressed as a percentage,(total units−acceptable units)/total units×100%. Percent yield may beexpressed as 100%−percent waste. Efficiency may be expressed as percentuptime×percent yield/100%. Percent reliability may be expressed assystem rate*percent uptime*percent yield/10,000%.

Another factor that may be used to characterize the productivity of amachine is the “Rate of Operation” (R/O), defined as the number ofnon-rejected standard unit products produced per hour (standard unitproducts is the number of products divided by a sector standardizedunit; for example, a standard unit of diapers could be set at 1,000, to50,000 diapers would be 50 standard units).

Machines or processes also may be evaluated in terms of opportunitycosts, which generally refer to the financial cost of waste or delay(include slow machine speed). As used herein, “Waste Opportunity Cost”is the direct cost of wasted products plus the delay cost. As usedherein, the “Delay Opportunity Cost” is the direct cost of machine downtime plus the cost of wasted product as a result of restarting themachine plus the cost of time that is spent disposing of wasted product.Also as used herein, the “Slow Running Opportunity Cost” is the cost ofthe machine running at a speed less than the ideal speed (determined ona per machine level), producing less product as a result.

To achieve standardized reporting, PIPE systems may provide informationabout production modes. Production modes may describe the status of amachine at any given moment, such as whether a machine is operating,down for scheduled maintenance, being used for a research run, and soforth. The production mode information from the PIPE system allows downtime or delays in production to be counted appropriately by financialdepartments. Thus, the PIPE output may include fields or records forproduction mode. In one embodiment, data entered into a production modefield are automatically screened for correctness (e.g., upon entry andagain upon the next start up), and errors or ambiguities are flagged forcorrection.

Other output parameters may be modified to comply with standarddefinitions required by finance or other users of the data. Thus, in oneembodiment, the integrated system and its method of use include a methodfor adapting an online production documentation system to providefinancial report data for a machine, including the steps of identifyingone or more key performance indicators pertaining to the machinerequired for a financial report, modifying the output of the productiondocumentation system to automatically track and generate the keyperformance indicators suitable for use in a financial report, receivingthe key performance indicator information from the productiondocumentation system, and incorporating the key performance indicatorinformation directly into a financial report such as an electronicreport (e.g., a web page or electronic chart). In one embodiment, thegenerated reports are maintained by the production documentation systemfor a preset time interval for future retrieval. In this manner,frequently requested reports may be delivered quickly with reducedprocessing overhead.

PIPE reports may be generated to report on operations on any of severallevels, such as at the level of section or subsection of a machine(e.g., monthly defects, waste, or delays in a film production linecaused by excessive arcing in a secondary corona treatment section of anapertured film line), at the machine level (e.g., hours of delay permonth for the entire film surface treatment converting machine, orpercent uptime for a lotion packaging line), for a product code (e.g., aparticular type of apertured films for use in sanitary napkins), at theplant level (e.g., percent waste for a film production plant, or ageneral plant summary), at the sector level (e.g., average percentuptime for all film plants in a sector), or at the corporate level(potential lost sales per quarter based on total waste and delay). Theleading (most frequent or most costly) types of waste or delay eventsmay be listed by machine, by section, by plant, by sector, and so forth.Detailed daily, weekly, monthly, or annual reports by machine, plant, orsector may be generated, and may be applied to specific products orproduct categories. Production, waste, or delay by shift or crew mayalso be reported.

Applications of PIPE financial information to the general ledger andvarious subledgers may be achieved via Charts of Accounts and othertools, as described by Dan Hughes, “Designing the Financial DataWarehouse.”

In the past, there was typically a significant delay between theacquisition of data pertaining to productivity, loss, or waste for amachine and the generation of a corresponding report for review bymanagement or incorporation into Corporate reports. Further, suchreports were generally limited in terms of what could be displayed,often being static reports rather than live, customizable reports. Thescope of the present invention includes an automated reporting systemadapted to provide more timely and flexible reports based on PIPE datawhich may be provided to management, incorporated into corporate reportsor intranet pages, used to call for remedial action or other decisionmaking processes, and the like.

In one embodiment, a method for automatically generating an alertcomprising a financial report based on event data comprises:

-   -   a) setting alert criteria for automatic report generation of an        alert, such a setting including a cost threshold for a        predetermined unit of time (e.g., a shift, day, or week, or        moving time frames such as the past hour, 24 hours, 3 days,        week, and so forth), such as the total cost of waste and delay        during the unit of time, the total cost of waste and delay from        a specified subcategory of event types during the unit of time        (e.g., web breaks or equipment failure), or, rather than        considering costs over a unit of time, also or alternatively        setting a threshold for the cost of any single event or any        event of a predetermined type (e.g., generate an alert if any        waste event has a cost of $2,000 or greater, or results in a        lost of at least 500 units of production);    -   b) repeatedly calculating costs for events during manufacturing        based on event information being recorded in a PIPE database        associated with the manufacturing process, the costs being        calculated for the events and periods of time specified in the        alert criteria, and comparing the costs to the alert criteria to        determine if the alert criteria have been met;    -   c) in response to the alert criteria being met, automatically        generating an alert comprising (or directing attention to) an        electronic financial report conveying information pertaining to        the costs that have met the alert criteria, and issuing the        alert electronically to a supervisor.

For example, the alert may comprise a message indicating that cumulativewaste or delay events during a predetermined period of time haveexceeded a specified threshold, and provide a chart showing the top tencategories of waste and delay events in terms of cost, or a table ofevents showing the nature and cost of the most expensive events or allevents that contributed to the alert. The financial report may compriseinteractive electronic information such as a bar graph with electroniccontrols (drop-down box, radio buttons, etc.) to allow the viewer tocontrol the format and content of the displayed information (e.g.,selecting the top N waster or delay events as a function ofuser-selectable periods of time, product categories, machine sections,shifts, and the like). The alert may be sent by e-mail, or anotherelectronic notification means may direct the viewer to use a link to thefinancial report information that is provided separately from thenotice. The user, who may be a supervisor or executive, may then callfor remedial action to deal with possible causes of the productionproblems that led to issuance of the alert. In one embodiment, themethod further comprises automatically indicating one or more possibleremedial actions that may be taken to reduce the production problem. Theindicated remedial actions may be suggested by an expert system or othermeans, and information on the costs associated with the remedial actionmay be automatically included to enable better or more rapiddecision-making.

The time delay between the occurrence of an event that contributes to acost threshold being exceeded and subsequent issuance of an alertcoupled with access to electronic financial reports based on event datamay be arbitrarily short. The time delay between events and reportsaccording to the present invention may less than a day, less than eighthours, less than an hour, less than ten minutes, less than threeminutes, or less than a minute. Indeed, live reports may includefinancial information about events that have occurred only a few secondsbefore generation of the live report.

The time frame for computation of cumulative costs may be a moving timeframe, whose endpoint continually advances in time (e.g., a span of oneweek ending with the current time), or a fixed time frame, with fixedstarting and end points, such as the days, weeks or months of thecalendar.

Alerts comprising electronic reports may also be issued to appropriatepersonnel in response to other information extracted by analysis ofevent data in the PIPE database. An increased rate of occurrence of onetype of event may, for example, be indicative of excessive wear of amachine component. Not only may an alert be sent to maintenance staffthat a component is in need of replacement, but the report system may beconfigured to automatically compile historical event data associatedwith that particular component of the machine to calculate historicaland recent or projected maintenance costs for that component to allow asupervisor to assess the need for improvements in machine or componentdesign to reduce costs associated with maintenance of the component. Inone embodiment, a report comprising historical cost informationassociated with the performance and/or maintenance of a machinecomponent (including the entire machine itself) is generated whenproblems with the component's performance or maintenance appear to becausing waste and delay at a rate or level beyond a predeterminedthreshold. In that case, management may be alerted that unusual orunanticipated costs are being accrued and that remedial action may beneeded. Again, an expert system may recommend remedial action andinclude information on the costs associated with the remedial action toenable better or more rapid decision-making.

A subset of the PIPE system, herein referred to as STORM (System forTracking Online Raw Materials), may be used to provide databaseinformation about raw materials accepted by a plant for use during theproduction of a product. The STORM system may provide access to rawmaterial properties, vendor information, and so forth. Productivity dataobtained by the PIPE system for a product may be combined with rawmaterial information from STORM to provide archived information aboutthe ingredients of a product, to permit analysis of the effect ofvarious raw material attributes on the productivity of the process orthe quality of the resulting products, and so forth. Possible functionsof the STORM system in the context of the present invention may include:

-   -   Tracking and reporting consumed raw material.    -   Linking raw material data to finished or intermediate products.    -   Validating raw material (e.g., shutting down the machine if an        incorrect raw material is loaded).    -   Collecting raw material waste data.    -   Rejecting and tracking reject material.    -   Tracking partially consumed raw materials (e.g., partially used        roll goods or bales).    -   Linking specific lots of raw material to machine waste and delay        results.    -   Linking specific raw material events (e.g., splicing) to machine        waste and delay results.

STORM may employ a separate database of raw material information thatmay be linked to a PIPE database and software. Raw material or pointersto such data may be integrated as a component of a PIPE database, ifdesired. A related system for electronically tracking materialproperties of raw materials and generating certificates of analysis fortheir use is disclosed in commonly owned U.S. patent application Ser.No. 10/253,200, “Supplier Data Management System,” filed Sep. 23, 2002by Amy H. Boyd et al., herein incorporated by reference. In this system,raw material data and certificates of correction, as well as informationabout product specifications, delivery and use dates and locations,etc., may all be included in the PIPE database or linked to data in thePIPE database.

In general, electronic means of receiving and processing raw materialdata in order to create electronic certificates of analysis may beintegrated with PIPE such that the PIPE database provides access to acertificate of analysis or a link (pointer) to the certificate and itsassociated data (vendor, manufacture date, raw material properties, testmethods used, batch number, date of receipt, etc.), such that the rawmaterial data may be considered in subsequent analysis of delay or wastebased on the PIPE database.

In one embodiment, a raw materials database (e.g., a certificate ofanalysis database) is used to store and merge raw material data. Thedata may be provided by a vendor or collected by the manufacturer orboth. For converting operations with roll goods, for example, the datamay be collected in three steps of the converting process: materialload, material start (or splice on), and material expire (or spliceoff). Prior to loading a raw material onto the converting machine,material label information is transferred to the raw materials database(such as from label bar codes using bar code scanners). This includesmaterial label information such as part number, lot number, andquantity. The converting line keeps a product counter that resets at afixed preset. This product counter is a reference number that istransferred to the database on certain machine events. At the time amaterial starts to be consumed and at the time a material expires theconverting machine transfers reference information such as a timestampand product count to the database. This information gets merged with thelabel information.

Another source of data that may be combined with a PIPE system is adatabase of consumer complaints or other post-manufacturing qualityindicators. Many producers of consumer products and other goods maintainone or more databases of information obtained from users of products,either from users or consumers contacting the manufacturer to register acomplaint (e.g., data logged by customer service representatives,including type of complaint and lot number of the product, if available,or date and place of purchase to help identify the time period ofmanufacture), or from surveys of users, focus groups, test markets,responses to targeted promotions, and so forth. Such data, whenassociated with lot numbers or other information regarding themanufacture of the product, may be linked to the corresponding PIPEdata. Establishing a link between post-manufacturing quality measuresand the PIPE database may permit data analysis to be performed toidentify possible relationships between operating conditions andconsumer complaints or other measures of quality.

The PIPE system and other related systems disclosed herein, as well asmethods of using such systems for improved productivity, financialreporting, raw materials handling, system optimization, and the like,may be applied to any manufacturing system, including continuous, batch,and semi-continuous manufacturing operations. The present invention maybe adapted for a single unit operation, a single machine, a series ofunit operations or machines, a group of related or unrelated machines ata single production facility (plant or mill), groups of productionfacilities (for all production operations or a subset thereof, such asoperations of a single type or for a single product), or forcorporate-wide operations for all products or a subset of products andprocesses. Exemplary products include cosmetics and toiletries, healthcare products, absorbent articles such as diapers or feminine careproducts, foods such as baby food or canned goods, paper and tissueproducts, pharmaceutical products, automobiles, electronic goods,petrochemicals, agricultural products, wood products, textiles,plastics, and the like. In one embodiment, the PIPE system, includingany of the STORM system, the PipeMap utility, and the PIPE Data logger,may be adapted for products produced under regulatory guidelines such asFDA regulations, and includes audit tools needed for Good ManufacturingPractices (GMP). For example, pertinent data from the PIPE system andother sources may be archived and verified with electronic signatures.Raw materials sources and their certificates of analysis may be recordedelectronically and associated with the materials produced. Informationregarding the recipes, materials, process conditions, crewmembers, andother issues may be electronically recorded and associated with thearchived data for future audits or reviews.

Tracking Delay and Waste

The PIPE system may be used to track delay and waste, or otherproductivity problems, as well as the apparent causes of those problems.

As used herein, “delay time” for a machine is any time when products arenot being made during a time that was scheduled for production of aproduct. Even if the delay is due to circumstances outside the controlof the company or plant, such as a shipment of raw materials from avendor that has been delayed due to bad weather or that was shipped tothe wrong plant due to a clerical error on the part of the vendor, theresult is still delay of production.

Whenever a converting line stops, a delay record is created in thedatabase. The record may include fields such as delay code, delayduration, timestamp, product count, and other information from themachine controller (see FIG. 10A and FIG. 10B).

In addition to delay data, waste data may also be obtained in much thesame way. Whenever defective product is culled (e.g., culled in responseto machine vision sensors in a converting line), a waste record iscreated in the database using a waste code, number of defects,timestamp, product count, and other information from the machinecontroller (see FIG. 10A and FIG. 10B).

In general, waste and delay information, as well as other productivityparameters, may be automatically captured on an event basis and storedin the PIPE database.

Productivity and performance of a machine, plant, or business unit maybe reported using any suitable set of measures. For example, the totalavailable hours for a reporting period (typically taken as 24 hours perday multiplied by the number of days in the reporting period) may bereported in terms of several categories, such as:

-   -   Development outages, which may include down time for special        research runs;    -   Market-driven curtailment, when a machine is taken down        deliberately because of inadequate sales or due to inventory        factors;    -   Planned asset outages, as approved by manufacturing leadership;    -   Planned holiday shutdowns;    -   Force majeure, when an uncontrollable event precludes operation,        including floods, hurricanes, disruption of energy supply, etc.        Catastrophic equipment failure for reasons other than acts of        nature would not normally be included under force majeure.

The total available hours, minus the sum of any hours falling into thefive categories immediately above (development outages, etc.) may betaken as the scheduled hours. The delay hours are the total number ofhours that the unit is precluded from operating for any reason duringscheduled hours. The scheduled hours minus the delay hours is the actualhours operated.

In one embodiment, “preliminary waste data” may also be obtained andstored by the PIPE system. “Preliminary waste data,” as used herein,refers to data regarding defects encountered or observed duringproduction of an intermediate product in a first process, wherein theintermediate product is intended for use as a raw material in a secondprocess, and wherein the defects did not cause waste in the firstprocess but are likely to cause waste in the second process. Thus, forexample, production of a roll of tissue for use as a barrier material ina diaper may lead to a PIPE table in the PIPE database describing eventsencountered during the production of the tissue, including machinevision or other sensor input pointing to a serious defect in productquality, such as a hole or tear in the web at a particular distance intothe web from the exposed outer end of the roll (e.g., 113 yards from theend of a 200-yard-long rolled web). The problem may not have created aneed for discarding the defective portion of the product, whichcontinued to be wound until the defective region was deep within a largeroll of tissue web ready for use in a diaper line. Thus, no waste ordelay was incurred, but a known problem has been detected in a productquality event that was recorded in the PIPE database for the product.When the product is subsequently used, the PIPE database may again beaccessed to alert the second process and its control system of aposition in the roll having a defect that will need to be eliminated byculling the affected product or culling the respective portion of theweb before it is incorporated into a final product. In other words, PIPEdata during a first process is used for feed-forward control of a secondprocess. The “preliminary waste” of the intermediate product thus becameactual waste in the final product, but with improved control over thesecond process.

The PIPE data may be correlated with process information and otherparameters, such as the nature of the shift or crew, material propertiesof raw materials, season of the year, etc., to better predict causes ofwaste and to better align machine operation and the “recipe” for theproduct being made to ensure the less waste is encountered in futureproduction efforts.

In one embodiment, the PIPE system provides a software application toallow users to view and add comments to the event records. The tools foradding comments may provide a customizable, multi-level menu structure(e.g., machine section, sub-section, problem, root cause, action, andcomment) for user entry of machine delay reasons. In one embodiment, aneural network system continually processes event records to mine thedatabase for information that may allow reduced waste. In anotherembodiment, a fuzzy logic expert system scans operator input to checkfor discrepancies, as well as to suggest improvements in operation toreduce waste and delay.

The PIPE system may also assist in identifying the various apparentcauses of delay. For example, when delay is due to force majeure thatpersists for a prolonged period of time, one may recognize that theproblem will persist and alter the schedule of time. In this case, onemay wish to only count as delay the first time unit in which the forcemajeure occurred, the time unit being chosen as desired from units suchas a shift of eight hours, a day, or other period of time.

The PIPE system may also account for down time due to marketcurtailment, wherein the machine has excess capacity due to inadequatecustomer orders, or because inventory of a product is sufficient tosupply customer demands for a period of time without producing moreproduct.

PIPE Fields and Tables

The PIPE database includes output tables and support tables with fieldsthat specify the machine and numerous aspects of the performance of amachine or process. The output tables include information obtained froma production event, such as a delay or waste event. Fields that may beof use in an output table for delay, by way of example, may include:

-   -   Machine Reference—a field identifying the machine    -   Timestamp—a field giving the date and time of the event    -   Delay Code—a field indicating the nature of a delay, which may        be related to the alarm in a programmable logic controller (PLC)        that caused the machine to stop. The delay code may be linked to        a particular PLC and machine section, and to a particular cause        of delay, or the field may be more general and be coupled with        additional fields for section and details of the delay.    -   Delay Trigger—a field indicating whether a delay code was caused        by a manual/operator stop.    -   Grade Shift—a field indicating when a shift in the grade of        production began (or other shifts in production parameters, such        as selection of raw materials, if desired), to serve as a        reference to a Grade Shift table, as illustrated below.    -   Operator comments—a field containing text entered by an        operator. Alternatively, this may be stored in a separate table        to which a link may be established based on the timestamp or        other information.    -   Duration—the length of a delay

The output tables may be supported by support tables (lookup tables ormaps) that are used to interpret information in the output tables andprovide links to other information in other databases or tables. Supporttables provide relatively static information to be used in conjunctionwith the active output tables. Support tables may be developed for delayevents, waste events, quality problems, and so forth. Exemplary fieldsin support tables for a delay event may include:

-   -   Delay Code—a field indicating the nature of a delay, which may        be related to the alarm in the PLC that caused the machine to        stop.    -   Description—a field that contains a brief description of the        Delay Code.    -   PLC Address—the PLC Address that relates to the Delay Code.    -   Section—the section of the machine in which the alarm was        generated    -   Machine Type—a field indicating the type of machine    -   Alarm Source—a field indicating where the alarm originated        (which PLC/processor)

The choice of how tables are constructed and linked, and which fieldsare used, may be the subject of many alternatives known to those skilledin the art. The specific examples shown for exemplary purposes here arenot intended to limit the scope of the invention.

By way of example only, a line of an output table may include the datashown in Table 1, which indicates what machine is being used, what codedescribes the delay, and when the delay occurred. The table alsoindicates when the current grade began being produced (the Grade ShiftStart time). Many other fields (not shown) may be present as well,including fields indicating what machine components were involved, whichcrew and shift was involved, what product was being made, which recipefile was being used, what corrective actions were taken, who made thecorrective actions, what the machine speed was prior to failure oraveraged during the time since the last event, whether any plannedmaintenance occurred, whether the down time was a scheduled down, etc.Other records may include data or provide links to data from selectedsensors for parameters such as air temperature, humidity, process waterpH, etc., which may be known to be relevant to runnability or quality. Afield for general operator comments may also be included (not shown).

TABLE 1 Portion of an Exemplary Delay Table. Machine Delay Ref. CodeTimestamp Duration (sec.) Grade Shift Start U2 257 9/22/01 04:12:48 2069/22/01 03:15:20

Further, a line of a support table serving as a delay map may includethe data shown in Table 2, wherein the meaning of code 257 in the DelayCode field is given.

TABLE 2 Portion of an Exemplary Delay Map. Delay Code Description PLCAddress 257 Web break at transfer to first B44:16/06 imprinting fabricon tissue machine U2

The description field may be more generic, such as “Web break attransfer to first imprinting fabric” so that it may be applicable tomore than one machine. It may also be simply “Web break” if the DelayCode were accompanied with additional information in the Delay Table tospecify where the break occurred.

Additional output and support tables may be used to link the informationin the Delay Table to other production data. A waste table may showwaste-related information, similar to that of Table 1 but usingparameters pertaining to waste. A material usage table may indicate whatraw materials were used and in what quantities for each period ofproduction. A Grade Shift table is an output table that may providebasic production information for the grade being produced. For example,an exemplary Grade Shift Table associated with the Delay Table of Table1 is shown in Table 3. The data shown indicate when the grade shiftbegan, what the machine was, which crew of employees ran the machine,how many rolls of product were produced for shipping, how many rollswere discarded as waste, and what the product was (e.g., white facialtissue according to recipe C2). A link between Table 1 and Table 3 ispossible by means of the Grade Shift Start field, which may serve aspointer in the Delay Table to additional information in the Grade ShiftTable. The Grade Shift Start value may be used combination with themachine reference field to serve as a pointer if multiple machines maybe considered. Thus, by looking up the entry in the Grade Shift Tablehaving the same Grade Shift Start field as that recorded for an event ina delay table, details of production information associated with thedelay may be obtained.

TABLE 3 Portion of an Exemplary Grade Shift Table. Grade Shift Mach.Waste Start Ref. Crew # Rolls Count Grade 9/22/01 U2 3 3380 96 White03:15:20 Facial C

PIPE data may also provide a continuous time series of machine stateinformation to show machine status and history before an alarm eventoccurs, or to allow tracking of the long-term effects of a processmodification on machine efficiency and modes of operation. The machinestate may be described by a Machine State record in a database, whichmay include labels such as startup, shutdown, thread, acceleration, fullspeed, etc. The state of operation may further be described byinformation from a statistical process control program, which maygenerate associated data to indicate at any point in time if the machinewas operating within specifications or whether it was out of control onone or more variables. Analysis of machine state data and other measuresof productivity in combination with statistical process control data maybe a rich source of information about the interaction betweenstatistical quality control practices and machine productivity.

Similar fields may be used to describe other events such as waste, slowdowns, process control excursions, quality problems, remedial actions tocorrect a delay, or other events. For example, an incident of waste on amachine (e.g., the culling of one or more products) may be described bya waste code may be associated with a description field, a PLC address,a section code, a delay trigger field, a machine type field, and analarm source.

If desired, PIPE data may be stored on a server at each plant and beperiodically “rolled up” to the corporate PIPE database. Support tablesor maps may be kept identical across a sector or across the corporationor other unit. Thus, the support tables or a subset of the supporttables may be maintained at the corporate level and provided toindividual plants to ensure uniformity. The plants may then ensure thatthe output of their PIPE systems is adapted to comply with theapplicable support tables.

Combining Human and Machine Input

To fully document the reasons for delay and optionally the correctiveactions taken, the PIPE system allows human input to supplementmachine-generated data for any event. Human-machine interfaces are oftenused for the entry of such data. Human input may be required to explainwhat the event was or to identify planned corrective action. Human inputmay also be required to validate a possible error state detected by acontrol system, or to select one of several automatic responses to adelay problem.

Human input is subject to many forms of error. For example, a shutdownmay be caused by a web break that normally takes 5 minutes to correct,after which time an operator may elect to initiate routine machinemaintenance for several hours. The source of the several-hour delay,when queried by the PIPE system, may be entered simply as “web break,”leading to a greatly inflated apparent cost of web breaks in thefinancial reporting for the shift, day, or other period of time in whichthe web break occurred. More accurate financial reporting may be done byensuring that the machine delay is properly identified, such asascribing five minutes of the delay to a web break and the remainingdelay time to routine maintenance.

For example, if a tissue machine stops due to a web break, whichnormally causes a delay of several minutes, an operator may choose toprolong the down time for other scheduled maintenance that might requirean hour. If the act of maintenance is not properly recorded, the hour ofdelay may be falsely credited to a web break. After a predeterminedperiod of time or after start-up, the PIPE system may then alert theoperator that the down time may have been incorrectly attributed to aweb break, and query if it was prolonged for other reasons, which maythen be recorded. Seemingly erroneous entries may also result in e-mailor other alerts directed to a supervisor for review and correctiveaction or further verification, if needed. Additional operator ormanagement input optionally may be required before the machine isallowed to start again to ensure that proper documentation is provided

To validate human input in this manner, an expert system may be used.The expert system may include a simple table of rules and responses todeal with common problems, or may be a more sophisticated fuzzy logicexpert system optionally coupled with a neural network that learns overtime how the process should perform and what conditions are anomalies.

Though fuzzy logic and neural networks may be powerful tools in datamining a PIPE database, it is to be understood that any knownstatistical or mathematical technique may be applied to determinecorrelations, find optimum process conditions, predict instabilities orrunnability problems, and the like. Such methods include statisticalanalysis such as regression or time-series analysis, signal processingtechniques such as autocorrelation analysis, etc.

The expert system may be an intelligent agent to automatically checkdata integrity as it is recorded in the database, adapted to tag therecord for human intervention if the data was suspect. If a data recordviolated a set of particular rules or was determined to be a statisticalanomaly, the agent may flag the record and send e-mail or othercommunications to appropriate people for intervention. If the record wasfound to be in error, it may be manually corrected; if the record wascorrect, a tag may be marked in the database to signal to the agent thatit had been checked and verified for accuracy.

The agent may be intelligent in two aspects. First, human experts mayimpart their learning to the agent through a fuzzy-rule-based inferencesystem. There are many types of errors in a machine process log thathumans may quickly and easily detect upon inspection. For example, amachine that made product during a particular day may report an averagemachine speed of zero due to a recording error. A person reviewing thisrecord may easily spot this inconsistency. A list of known errors andinconsistencies would be compiled into fuzzy if-then rules, and theagent may automatically navigate a large amount of data and check thedata using the expert-based rules. Second, the agent may use a neuralnetwork to learn patterns in the data. Deviations from learned patternsmay be flagged as anomalies. The neural network may be trained withhistorical data and may be re-trained after a given time period to beupdated with the most current process information.

Integration of Data for Multiple Systems

PIPE data from multiple machines or plants may be integrated andsummarized in a common display or report. Database results from multiplesources may also be sorted or searched in any way desired, such assorting waste data by geographical region and machine type, or searchingfor plants of a certain kind having waste delays in the upper quartile.

When PIPE is applied to multiple machines or plants for financialreporting of a plant, sector, or other business unit, there may be aneed to obtain useful information from a variety of control systems orhardware and software systems. One useful method for establishingcommunication and common standards between multiple machines and controlsystems is the use of maps that identify the relationship betweenparameters required by PIPE and the data structures employed by thediverse systems communicating with PIPE. Map definitions (e.g., supporttables) may be established by a central supervisor and then downloadedto the respective business units or plants, to ensure that the datatransmitted from the machines at the plants is in the proper fields andformat.

Feed-Forward Control Systems

When a raw material for use in a process was produced under a PIPEsystem of the present invention, electronic data in time series formabout production defects may be available that may be of value for aprocess control system. For example, during production of a roll ofcover material used in feminine care products, a PIPE system may recordthat defects were observed at two positions in the roll (e.g., at 210meters and 318 meters within a roll of material having a total length of500 meters). The defects may have been associated with a web break andrepresent the location of splices, or they may have been holes or colordefects that did not result in machine delay but were detected asquality problems that may or should result in waste during subsequentmanufacturing. The information about the nature of the defects and theirlocation in the roll is sent to the machine that subsequently processesthe roll as a raw material. A feed forward control system then allowsthe machine to anticipate the problem areas in the roll as they areabout to be fed into the machine or any of its unit operations. Themachine may, in response to the problem supplied by the PIPE system forthe raw material, slow down or invoke a cull to remove potentiallydefective product or initiate other compensating action. Thus, waste maybe predicted at an early stage and the cause may be properly identifiedwhen the material is culled. Through anticipating the problem, theimpact of the defects in the raw material on runnability andproductivity of the machine may be reduced (e.g., web breaks may beavoided, or other machine problems may be averted), while quality of thefinal product is improved.

Basic information identifying the raw materials used in production maybe supplemented with detailed information from an electronic certificateof analysis or other information accessible, for example, via a licenseplate system, described hereafter.

In one embodiment, the feed-forward system employs information obtainedfrom a subset of PIPE specifically engineered to track raw materials(e.g., STORM—System for Tracking Online Raw Materials). STORM enablesdetailed data about the production history of a material to be generatedduring production and stored.

In another example, in producing a roll of tissue, the STORM system mayprovide the quality attributes of the tissue, including a record ofmeasured basis weight from a beta-radiation-based scanner or other meansas a function of position in the roll, and perhaps a record of opticallydetected web defects in the roll, again as a function of position in theroll (distance from the end of the roll). The tissue may then be slittedand converted into multiple smaller rolls for use in a diaper mill, forexample. Each slitted roll may have an electronic file associated withit indicating the basis weight and presence of defects as a function ofroll position. When the roll is received at the mill, this informationmay be accessed by scanning a bar code to obtain an identifier thatlinks to the data file. The raw material data file is accessed byprocess control systems for the machine. The system may then anticipatethat a defect may exist at, for example, 47 meters into the roll. Themachine speed may be momentarily reduced to either prevent a web breakor to allow the defective portion to more easily be removed, after whichfull speed may be resume. If a portion of the roll has inadequate basisweight, that portion may be automatically spliced out by the machine orwith the assistance of human operators, following directionselectronically conveyed in response to the process control system of thepresent invention.

In general, STORM or PIPE data for one component (e.g., a raw materialor intermediate product), generated by any of the machines used in theproduction of that component, may be communicated to other machines thatuse the component in manufacturing. The data may be used to verifyquality of the incoming components, to make adjustments to the component(e.g., removing portions with quality problems), or to make adjustmentsto the machines using the component. In the latter case, feed forwardprocess control technology may be applied to adjust the machine inanticipation of changes in the component. Other suitable process controlstrategies may be used as well.

In one embodiment, the improved system may access multiple databasespertaining to the raw material using the “license plate” described morefully hereafter, in which a “license plate” bar code or other identifieron the material permits access to multiple databases of informationpertaining to the materials. In other words, the license plate may be apointer to multiple sources of data. The databases may have a commonformat for easy access to and display of information in a form usable bythe manufacturer.

The problem that is anticipated need not be an absolutely verifiedproblem, such as an observed defect, but may be one that is onlyprobable or possible based on a detected event that is known to beassociated with a quality problem (i.e., a deviation in the propertiesof the raw material). For example, based on past experience withmanufacturing a roll good on a first machine, it may have beendetermined through data mining or other procedures that after the firstmachine goes down, the portion of a web being produced that was incontact with a heated section of the first machine when the firstmachine went down may have a 25% probability of being thermally damagedduring the down time, resulting in an increased likelihood that the rawmaterial may fail in a subsequent manufacturing process on a secondmachine. The process conditions for the subsequent manufacturing processemploying the raw material may be temporarily adjusted near the timewhen the portion of the web in question is unwound and enters the secondmachine, in order to decrease the probability that a web break or otherfailure will occur. In this manner, the likelihood of waste or delay ora quality problem can be decreased in a manufacturing process bytemporarily adjusting process conditions responsive to previouslyobtained manufacturing information about a raw material, wherein themanufacturing information is interpreted to indicate an increasedprobability of a quality problem or waste or delay if normal processconditions are maintained during manufacture of a product.

Temporary adjustments to process conditions that anticipate possiblemanufacturing problems due to deviations in the properties of a rawmaterial may be done when a batch or unit of the raw material is“sequentially trackable,” meaning that data are available relating oneor more identifiable portions of a raw material (each portion comprisingsubstantially less than 100% of the raw material in this case, such as10% or less, or 2% or less) to manufacturing or material propertyinformation about the one or more portions of the raw material.Sequentially trackable raw materials are typically produced in a knownsequence and, in a subsequent manufacturing process, supplied in a knownsequence that can be related to the sequence of manufacturing. Forexample, roll goods can be sequentially trackable. When roll goods areused as a raw material, they are generally used in the reverse sequencein which they were manufactured (the last portion made is the firstportion used; though in some applications, the first portion made may bethe first portion used). Webs in any form may be sequentially trackable,including cut stacks of web materials, festoons, and the like. Materialsthat are provided in a string or other fixed sequence may also besequentially trackable (e.g., medications sealed in pouches along acontinuous web of aluminum foil). Material in bales, or loose powders orliquids in tanks, or vats generally is not sequentially trackablebecause the material within the batch becomes mixed after production.

A batch of sequentially trackable raw material may be associated with asingle identification code (e.g., a single barcode or single electronicproduct code from a smart tag for an entire roll of materials), but theidentification code may provide access to sequential manufacturinginformation such as event data in a PIPE database and/or continuouslymonitored process data from any number of process sensors and othercontrol devices, and the sequential information may then be associatedwith various portions of the raw material (e.g., identifying a basisweight deviation in a roll good at a specified location in the roll).

A bill of materials for the manufacturing of a product may specify ormay be consulted to help specify what actions should be taken for ananticipated temporary deviation in the properties of a raw material.Some deviations of the properties may still permit production of theproduct within the targeted specifications for that product, while otherdeviations may require culling of the products affected by the deviationin the properties of the raw material, or may require rejecting theaffected portion of the raw material so that it does not enter themachine.

The above-mentioned feed-forward system or system of machine-to-machinecommunication regarding raw materials and their use in products need notapply to components produced from a single manufacturer, but may alsoapply to any raw material used by a manufacturer, wherein data generatedby vendors of the raw materials are obtained and stored for use inmanufacturing systems according to the present invention.

Accessing the data may require a connection across a network involvingvendor computers. Alternatively, the vendor may electronically supplydata to a common manufacturer database that may later be accessedthrough the license plate system.

In another embodiment, probabilistic or “fuzzy” information may be usedfor improved feed-forward control. The probabilistic information may beobtained by correlations of past machine performance or product qualityas a function of PIPE data or raw material data for a component used ina process. The correlations may indicate that the risk of a delay orquality problem may be greater unless machine conditions are modified,or may indicate mixed risks and opportunities that may be weighed forthe greatest expected economic return. For example, in the production ofa diaper, correlations of past quality results could indicate that astatistically significant increase in consumer complaints about adhesivefailure occurred 35% of the time when, even though all productspecifications were met, when a batch of hot melt adhesive having amolecular weight slightly below target was used, suggesting that anincreased amount of adhesive may need to be used to secure a component,but at a higher cost. However, the PIPE data may also show thatincreasing the application level of adhesive historically results in a10% increase in down time due to adhesive nozzle plugging, and mayindicate that machine runnability improved on the average when the lowmolecular weight adhesive was used. These factors may be associated withtheir expected costs and optimized run conditions may be suggested orautomatically implemented, optionally subject to human supervision. Ingeneral, the information used for feed-forward control need not be datadirectly describing quality problems or other waste and delayinformation pertaining to components of a process, but may beinformation inferred from past PIPE and other data, such asprobabilistic predictions obtained by correlations or neural networkmining of the data to suggest opportunities to be obtained (increasedmachine speed, for example) or possible problems to be avoided orprobabilities of various costs and problems to be weighed in optimizingprocess conditions as the associated materials enter the system.

Data stored in process information databases such as PIPE enable thedetermination of non-obvious cause and effect relationships betweenmanufacturing events. While many events are related in a trivial manner(e.g., a raw material splice will cause some products to be discarded),there exits a real possibility that seemingly non-related events arecorrelated. The more non-obvious a correlation, the less likely it willbe discovered by a machine operator or process engineer. This isespecially true for events that are separated by some temporal distance(i.e., lag). Data mining techniques applied to the process informationdatabases provide an excellent method of uncovering non-obvious yethighly correlated events to suggest process modifications or rawmaterials strategies that offer a probability of improved performance.In addition to finding these cause and effect relationships, processinformation data mining also provides information than may be used forprocess troubleshooting. Exemplary methods for data mining are given inPredictive Data Mining: A Practical Guide by Sholom M. Weiss and NitinIndurkhya (San Francisco: Morgan Kaufmann Publishers, 1997), ISBN1-55860-403-0. Data mining may be done according to CRISP-DM standardsin “CRISP-DM 1.0: Step-by-step Data Mining Guide” by P. Chapman et al.Exemplary software tools for data mining include EDM (EnterpriseData-Miner) and DMSK (Data-Miner Software Kit), both available fromData-Miner Pty Ltd (Five Dock, Australia). Any known data visualizationor pattern detection tool may also be applied, such as the OMNIVIZsoftware system of OmniViz, Inc. (Maynard, Mass.).

Electronic “License Plate” Embodiments

In one embodiment, the PIPE system is adapted for use with a materialstracking system analogous to the use of license plates for automobiles,where a single code (the license plate ID) may be used to uniquelyidentify an object such as an automobile and its owner, or, in thepresent invention, a raw material lot. Just as a license plate on anautomobile may be used to identify its owner and thus to accessinformation from multiple databases pertaining to the owner, so may anelectronic license plate uniquely identify a batch or lot of a rawmaterial and thereby allow access to one or more databases ofinformation pertaining to the raw material. The data accessible by meansof the electronic license plate (e.g., upon scanning a bar codeincluding the electronic license plate identifier) may include the PIPEdata associated with the manufacturing process in which the raw materialwas used.

In past procedures for handling raw materials in the production of anarticle, a label with one or more bar codes is typically applied to acontainer or to the material itself to provide information about thesource and properties of the material. Those using the material inmanufacturing scan the bar code to extract information. Multiple scansare often needed to extract information from multiple bar codes on alabel, such as bar codes for manufacturer item number, vendor lotnumber, and quantity. The information provided by the labels to themanufacturer is limited and may be inefficient to use.

In the improved method for handling and tracking raw materials,according to the present invention, the “license plate” concept permitsintegration of data from multiple vendors or, more generally, from thesources of multiple materials used in a product. Instead of bar codesproviding a small amount of data that must be scanned multiple times, a“license plate” code or other identifier on the material permits accessto multiple databases of information pertaining to the materials. Inother words, the electronic license plate may be a pointer to multiplesources of data. The databases may have a common format for easy accessto and display of information in a form usable by the manufacturer.

In another analogy, the “license plate” code of the new method is to aconventional bar code as a hypertext link on a web page is toconventional printed text. The “electronic license plate” code mayprovide access to all the information of a conventional bar code, butadditionally may provide for rapid electronic access to vendor databasesor other databases giving detailed information about the material. Suchinformation may include electronic certificates of analysis, such asthose generated by the Supplier Data Management System (seecommonly-owned U.S. patent application Ser. No. 10/253,200, “SupplierData Management System,” filed Sep. 23, 2001, previously incorporatedherein by reference), and the associated tables of material propertiesand quality statistics. Such information may further include informationabout quality control during manufacturing (e.g., data from a processcontrol system or other quality parameters and time-series of raw data),the materials used in producing the material, operating parameters(target and actual), etc. Thus, for each pallet, roll good, raw materialsource, or intermediate product from the beginning to the end ofproduction, there would be quality attributes, production history, andother information that may be tracked and linked to manufacturing eventsand final product quality.

The license plate code may be provided in an optically scannable barcode or other optically scannable marks such as compressed symbologymarks that may be read by a charge-coupled-device (CCD) video camera orother optical scanning means. For example, the “QR Code” (Quick ResponseTwo-Dimensional Code) of Toyota Central R&D Laboratories, Inc.(Nagakute, Japan) may be used. Another form of a compressed symbologysystem is the DATAMATRIX of RVSI Acuity CiMatrix (Canton, Mass.).Related scanning equipment includes the DMx AutoID fixed positionscanner, the MXi hand-held scanner, and the Hawkeye 30 hand-heldscanner, all available from RVSI Acuity CiMatrix. The compressedsymbology mark may be printed directly on an exposed surface of aproduct or on the core of a roll good, a pallet or wrap, and so forth,or may be printed on an adhesive label that is affixed to the rawmaterial or associated packaging. The marking need not be visible to thehuman eye, but may comprise an ink that fluoresces in ultraviolet (UV)light, for example, or the marking may be covered with a coating thatstill permits scanning of the underlying marking.

The identification code may be conveyed by other electronic means suchas radio signals (including ultra-wide band signals), and readableelectronic storage devices such as smart cards, electronic chips, ormagnetic storage media. In one embodiment, a package or shipment of araw material is labeled with smart tags that may emit a radio signalcarrying an electronic code which may either directly convey informationabout the raw materials, or provide an identifying code, which may beused to retrieve information about the raw materials in a database. Thecode on the smart tag may be read by a scanner, which may be a portabledevice that is brought near to the smart tag to obtain a reading, or thereader may be a stationary device, which reads the smart tags as theyare brought to the reader. An electronic product code comprising thelicense plate code may be read by the scanner with a readable rangetypically on the order of a few feet, though broader or narrower rangesare possible.

RFID smart tag technology is known and understood by those skilled inthe art, and a detailed explanation thereof is not necessary forpurposes of describing the method and system according to the presentinvention. Generally, conductive or passive smart tags consist of asemiconductor, a coiled, etched, or stamped antennae, a capacitor, and asubstrate on which the components are mounted or embedded. A protectivecovering is typically used to encapsulate and seal the substrate.Inductive or passive smart tags have been introduced by Motorola underthe name “BiStatix”. A detailed description of the BiStatix device maybe found in U.S. Pat. No. 6,259,367 B1. Another commercial source ofsuitable smart tags is Alien Technology Corporation of Morgan Hill,Calif., under the technology name FSA (Fluidic Self-Assembly). With theFSA process, tiny semi-conductor devices are assembled into rolls offlexible plastic. The resulting “smart” substrate may be attached orembedded in a variety of surfaces. The smart tag technology underdevelopment at the Auto-ID Center at Massachusetts Institute ofTechnology (Cambridge, Mass.) may also be used within the scope of thepresent invention. Further information on smart tags and relatedtechnology is disclosed in U.S. Pat. No. 6,451,154, “RFID ManufacturingConcepts,” issued Sep. 17, 2002 to Grabau et al.; U.S. Pat. No.6,354,493, “System and Method for Finding a Specific RFID Tagged ArticleLocated in a Plurality of RFID Tagged Articles,” issued Mar. 12, 2002 toMon; PCT publication WO 02/48955, published Jun. 20, 2002; U.S. Pat. No.6,362,738, “Reader for Use in a Radio Frequency Identification Systemand Method,” issued Mar. 26, 2002 to Vega; D. McFarlane, “Auto-ID BasedControl,” White Paper for the Auto-ID Centre Institute forManufacturing, University of Cambridge, Cambridge, United Kingdom, Feb.1, 2002; and Chien Yaw Wong, “Integration of Auto-ID Tagging System withHolonic Manufacturing Systems,” White Paper for the Auto-ID CentreInstitute for Manufacturing, University of Cambridge, Cambridge, UnitedKingdom, September 2001.

Other RFID technologies believed to be of value for the presentinvention include the I*CODE chips and readers of Philips Semiconductor(Eindhoven, The Netherlands); the RFID tags of Sokymat (Lausanne,Switzerland); and the RFID technology of Texas Instruments (Dallas,Tex.) including their TI*RFID systems.

Gemplus (Gemenos, France) provides smart tags (sometimes called “smartlabels”) and smart cards employing RFID technology, which may be used assmart tags. They also market interfaces, antennas, scanners and softwarethat may be adapted for use with smart tags.

With RFID or other smart tag technology, a vendor may associate a uniqueID code with a batch of raw materials, and enter physical property datainto a database in which the data is associated with the ID code. Whenthe raw material shipment is received, an RFID scanner may automaticallyscan the RFID chip and retrieve the associated information from thedatabase, verify that usable raw material has been received at thecorrect facility, provide quality information to be associated with thePIPE database, and so forth.

It is to be understood that many other technologies are potentialsubstitutes for the RFID embodiments disclosed herein. For example, RFIDreaders could be replaced with optical scanners, image analysis devices,arrays of chemical detection devices, and the like to allow othertechnologies for reading identification means to be applied.

A related technology within the scope of the present invention isSurface Acoustic Wave (SAW) technology. For example, InfoRay (Cambridge,Mass.) markets a passive smart tag that is said to achieve long ranges(up to 30 meters) using a Surface Acoustic Wave (SAW) device on a chipcoupled with an antenna. The SAW device converts a radio signal to anacoustic wave, modulates it with an ID code, then transforms it toanother radio signal that is emitted by the smart tag and read by ascanner. The ID code of the smart tag is extracted from the radiosignal. RFSAW, Inc. (Dallas, Tex.) also provides minute Surface AcousticWave (SAW) RFID devices that may be used within the scope of the presentinvention.

Ultra-wide band (UWB) technology is related, in that it permits wirelesscommunication between objects using low-power electromagnetictransmissions. However, receivers and transmitters generally are bothactive but use very low power, typically less than that of radiofrequency noise, relying on intermittent pulses which cover a broad bandof frequencies rather than transmissions of a particular frequency. UWBtechnology may provide much higher spatial capacity (informationtransmission per unit area) than other wireless standards such asBLUETOOTH brand computer communication services or Institute ofElectronics and Electrical Engineering (IEEE) 802.11a or 802.11b.

The license plate system may be integrated with the STORM system withinPIPE. The system may be structured to support multiple converting linesin multiple plants as an enterprise information system.

In one example, when a shipment is received at a warehouse, a worker orelectronic device scans the license plate code and then a workerobserves a computer screen. The screen displays what the material is,who the supplier is, when it was shipped, etc., and optionally maydisplay a certificate of analysis showing its suitability for use in theintended process. The scanned identifier provides access to one or moreadditional databases that may be accessed immediately by the operator,as needed, or later as part of an audit. For example, operators usingthe material may access manufacturing history information or productquality information to troubleshoot the use of the material. Informationmay be archived as part of a GMP system.

In one embodiment, to effectively implement the license plate system,the vendors or others whose data will be accessed provide the data in acommon format according to the parameters that are needed by themanufacturer. Thus, particular data fields and their units may bespecified to establish a common format, as well as other format aspectsof the database.

Accessing the data may require a connection across a network involvingvendor computers. Alternatively, the vendor may electronically supplydata to a common manufacturer database that may later be accessedthrough the license plate system.

In one embodiment, the electronic license plate for a raw material isscanned and used to access a database providing links to tables in otherdatabases containing information about the raw material. As the rawmaterial is used in a manufacturing process, a new entry in a databaseis created (or a former data record is supplemented) to contain a linkto PIPE data for the manufacturing process, or to contain a copy of allor part of the PIPE data itself. Then, long after the raw material hasbeen used, subsequent users may retrieve information about the rawmaterial and the performance of the manufacturing process in which itwas used. This linkage of PIPE data with raw material data via a rawmaterial license plate may allow analysis of the manufacturing data tobe done to correlate raw material properties with productivity. Forexample, data mining with a neural network or any other known datamining method may be used after the fact to determine relationshipsbetween raw material properties or sources of origin and productivity onmachines or in processes using the raw material. It may be found, forexample, a polyolefin having a molecular weight falling within the lowerend of the acceptable range, per current specifications, results in 10%more down time in a meltblown operation than similar materials having ahigher molecular weight, also in the acceptable range. Such data miningefforts may then lead to a revised raw material specification to improvematerial properties.

In another embodiment, the raw material property data provided by avendor are combined (either by copying of data or providing links to thedata) with PIPE data for production of an intermediate product, and thenmade available for feed-forward process control of a second process formanufacture of a final product.

In practice, the license plate ID number may include two parts, a vendorcode assigned by the manufacturer to the vendor, and an additional batchcode created by the vendor to identify the batch of raw material (e.g.,a roll of material, a barrel of fluid, a bale, or other unit). The batchcode may be further subdivided. For example, the vendor code may includea predetermined number of bits, such as 16 bits. The batch code mayinclude another predetermined number or variable number of bits, such as12, 16, 24, or 32. A 24-bit batch code and a 16-bit vendor code may becombined into a single 40-bit license plate code provided as a bar codeor other scannable code (including machine-readable Roman numerals oralpha-numeric symbols, or another script such as simplified Chinesecharacters or Arabic script).

By allowing the vendor to create a license plate identification, basedon a manufacturer-supplied vendor code and a vendor-supplied batch code,the efficiency of handling raw materials is improved. In past practice,manufacturers often would add their own label to a batch of material fortracking its use and properties, in spite of a label having already beenapplied from the vendor. Two or more labels may have been scanned andprocessed in such operations, with data associated with each label beingsubstantially disconnected. The license plate system of the presentinvention allows a single label to serve the needs of the vendor andmanufacturer, providing access for the manufacturer to all needed dataand providing means for tracking the performance of the raw material insubsequent manufacturing processes through connection to the PIPEdatabase. Double labeling or the scanning of plural bar codes for asingle batch of product is no longer needed, according to severalembodiments of the present invention.

For intelligent manufacturing, smart tags or UWB systems may be adaptedfor other uses as well, such as automating the tracking of positions ofmachine components and correlating portions of a moving device such as abelt or wire with production problems. For example, microscopic smarttags may be embedded at various locations in a papermaking fabric, andan RFID reader in a papermaking machine could monitor which portion ofthe fabric is contact with a paper web as the web passes through a nip,enters a dryer, or is transferred to a Yankee cylinder, for example.Fabric position could be entered into a PIPE database when there iswaste and delay, and a fuzzy logic analyzer or known statistical toolscould then be applied to look for correlations between fabric positionand various runnability or quality control issues. For example, PIPEdata may indicate that web breaks at the creping blade are 50% morelikely for tissue that had been in contact with the seam portion of apapermaking fabric, and steps may then be taken to improve the seamregion of the fabric or to momentarily reduce web tension when tissuethat contact a seamed region is about to reeled from the Yankee.Alternatively, tracking of the position of airfelt forming drums orother moving components in an absorbent article production facility maybe combined with PIPE data to determine if particular portions of amoving system are more subject to waste and delay problems, therebysignaling the need for corrective actions such as repairs ormodification of production methods.

Embedded smart tags in wires, belts, or other machine components mayalso be used to identify incipient failure or degraded performance dueto wear or other mechanical problems. The incipient problem may bedetected by the loss or failure of the smart tag, such that theinability of a scanner to read a smart tag at a particular location(e.g., the absence of the signal generated by a functioning smart tag)is the signal that a problem or other undesirable process condition hasoccurred or may occur soon. For example, a smart tag embedded in a drivebelt may be lost when the belt becomes worn and is in need ofreplacement, providing a silent or passive signal that heavy wear hasoccurred. The degree of wear in many moving and stationary parts may becontinuously monitored in this manner, and information about missingsmart tags in such parts may be converted to estimates of wear or othermeasures of machine condition, and this data may be archived andincluded with or associated with process event data in the PIPEdatabase.

Further, all operations with bar codes may be replaced with smart tagsor UWB identification devices. For example, instead of using a bar codeto identify a batch of raw material shipped to a production facility ina supplier data management system, a smart tag embedded in a the rawmaterial or the raw material packaging could be automatically read whenthe raw material is received at the production facility, and theelectronic product code generated could either uniquely identify the rawmaterial, allowing links to online material property attributes and acertificate of analysis to be accessed, or the smart tag could beprogrammed to contain the needed information, such as MSDS data, anelectronic certificate of analysis, purchase order information, and thelike. Intermediate materials such as nonwoven webs in roll form may beforwarded to the next operation with an identifying smart tag in thecore of the roll which may contain provide a unique identifying code forthe roll, which in turn may be uniquely paired with a URL or databaseaddress from which information about the production of the material maybe made available for feed forward control during manufacturing. Forexample, the location of a splice in the role may be indicated, allowingequipment to adjust speed and tension appropriately during use of thematerial to prevent a break when the splice is unwound and entersproduction.

In addition, bills of materials may be automatically checked byverifying that proper raw materials have been loaded, based on RFIDscanners reading smart tags of the raw materials as they are loaded foruse in production. Inventory management may also be simplified by usingUWB transmitters or smart tags to track the physical location of rolls,pallets, or boxes of materials in a warehouse or other facility. WithUWB devices, triangulation of an emitted signal may permit location ofits source, much as in GPS systems. With RFID technology, scanners anddetectors may read and record the location of numerous products in astorage facility, either by passing a scanner through the facility or byhaving multiple scanners in the facility that detect objects within ashort distance of the scanner. In addition, smart chips or UWB devicesworn or carried by the operators may be used in lieu of a paperchecklist to record the completion of general housekeeping duties,machine health checks, or other actions required by Good ManufacturingPractices.

In another embodiment, smart chips or UWB devices worn or carried byoperators may be used to track and record actions of specific operators.For example, a smart tag identifying an operator may be read by thevarious input and control devices associated with an EWMA system orother HMI (human-machine interface) systems (e.g., a distributed controlsystem) to verify the identity of the operator. If the operator enters arestricted area or physically modifies a portion of the machine, RFIDreaders in certain locations of interest may track the physical presenceof the operator and may associate that operator with changes made to themachine during that time and in that location, for possible subsequenttroubleshooting or problem solving analysis.

By way of example, RFID tag and RFID readers, under the name Intellitag500, may be purchased from Intermec Technologies Corporation of Everett,Wash., and Intermec's Amtech Systems Division in Albuquerque, N. Mex.,or the RFID reader may be a Commander 320 13.56 MHz RFID reader,manufactured by Texas Instruments of Dallas, Tex. Other automaticidentification and object tracking systems may be used such as RF SAW(radio frequency surface acoustic wave) technology from RF SAW, Inc.(Dallas, Tex.).

PIPE and a Bill of Materials System

The productivity of a machine or plant may be improved with an automatedBill of Materials (BOM) system in which “recipes” or other productspecifications are used to govern machine operation and raw materialsacceptance for a targeted product. These recipes, which are available inelectronic form, are used to identify the correct combinations ofprocesses and materials that are needed for various products and toautomatically ensure that the incoming raw materials and machinesettings are appropriate. Information in the form of bar codes or othermeans may be used to track the components and their attributes to ensurethe recipe was properly followed. Various components may beauthenticated and their interchangeability may be known and properlyaccounted for when replacement materials were needed. In one embodiment,the bill of materials includes at least one specification formanufacturing a product (e.g., a machine setting or a materialspecification).

For example, before a raw material at a plant is loaded or otherwiseused in a process for the manufacture of a product, the bar code of thematerial may be scanned to obtain information about the raw material,including information from a license plate system which providespointers to various databases based on a single identification code forthe batch of raw material, or information from an electronic certificateof analysis. The raw material information is compared with the currentproduct's bill of materials (recipe). A message is returned anddisplayed indicating whether the material is valid or invalid for itsintended use. When an incorrect material has been selected for use inthe process, the PIPE system (or, in more particular embodiments, thePIPE Event Logger, or, most specifically, the event logger module of thePIPE Event Logger) may shut down the machine or process until thecorrect material is loaded, or until an authorized and justifiedoverride is applied, wherein the reason for the override (e.g., ajustification) may be required to be entered for auditing purposes(e.g., as an override code).

In one embodiment, a control system such as control system 54 in FIG. 1,in cooperative association with the PIPE Event Logger 58 and an IDreader 50 for raw materials, functions as the BOM system describedherein. In such an embodiment, the control system obtains the bill ofmaterials for a product, obtains data associated with at least onematerial input to the process, and compares the obtained data with theobtained bill of materials. If the obtained data exceeds a presetthreshold identified in the bill of materials or is otherwise invalid,the control system invokes a setting change or other modification of theprocess to prevent waste and/or delay. For example, the control systemmay disable the machine associated with the process.

The BOM system may be integrated with the STORM system (System forTracking Online Raw Materials) of PIPE and may support multiplemanufacturing or converting lines in multiple plants as part of anenterprise information system. The BOM system may access data fromelectronic “license plates,” or pointers to one or more databasesproviding information about the material in question. The BOM system mayalso be integrated or used in conjunction with a Supplier DataManagement System that generates electronic certificates of analysis forincoming raw materials.

Each product may have a standard recipe available in electronic formatfrom a database. The recipe specifies what raw materials are needed(e.g., material type, characteristics, etc.) and may specify how theyare to be used. When a material is brought into the production line, itmay be scanned or a material code may be entered, and software may thenaccess specifications for the material and compare it to the recipe. Ifthere is not a suitable match, the machine may be shut down until thecorrect raw materials are provided. Optionally, the recipe may providedirections for machine settings (speed, temperature, etc., of variousdevices) and may automatically invoke machine changes or, if desired,require employees to make the appropriate adjustments.

Accessing the data may require a connection across a network involvingvendor computers. Alternatively, the vendor may electronically supplydata to a common manufacturer database that may later be accessedthrough the license plate system.

When the recipe demands are met by the incoming raw materials, themachine is allowed to operate and an electronic record is created tospecify the starting materials used and the production history for therun. PIPE data are continually created in the process as the machineoperates. All details of raw materials and their correspondence tospecified guidelines may be recorded and archived to provide an audittrail for GMP compliance.

During grade transitions, the BOM system can compare presently loadedraw materials with those of the Bill of Materials for the new grade anddetermine what changes are needed. The BOM system can also be adapted toimprove performance of a process during grade changes. In manymanufacturing systems, there are multiple raw materials that must bechanged when a product grade is changed. Some raw material changes canbe as simple as taking a roll of material off a spindle and replacing itwith a new roll, or switching a valve to change a chemical sprayed ontoa product, but in some cases there may be tanks or lengths of pipingfilled with a previous raw material or product of a previous rawmaterial which may require time to be flushed out with a new rawmaterial (or product of a new raw material) before the system can fullymeet the specifications for the new grade, resulting in lost productionor production of lower grade product. The BOM system can be adapted tooptimize grade changes by including recipes for intermediate productsthat can be produced during the transition from one grade to another.For example, a transition from a red-colored pasta grade to agreen-colored pasta grade may result in off-color product made duringthe transition from red to green, but there may be a marketable“transition” product in which color of the pasta is not important. Thetransition product may be produced with reduced waste during thetransition. To be marketable, the transition product made during thetransition may require adjustment in other ingredients or processconditions that have a faster response time (or flush time) than the rawmaterial lines that need to be changed to achieve the targeted gradechange. Thus, a transitional bill of materials can be specified to allowa marketable product to be made during a grade transition requiring araw material change in which other raw materials may be temporarilychanged during the transition to achieve a marketable intermediateproduct. Transitional bills of materials may be predetermined for avariety of grade transitions, or may be determined by an expert systemor by optimization of costs considering the various alternative rawmaterials that may be acceptable for a new targeted grade and a proposedtransition product, as well as considering the quantities of rawmaterials from a previous grade that are currently in place and thecosts of changing the previous raw materials. In some cases, forexample, it may be more cost effective to continue producing excessproduct to use up a raw material rather than to have to remove theremaining raw material to prepare for an immediate grade change.

The PIPE and BOM systems may record the transition events (e.g., end andbeginning of a run), record the recipes used (e.g., initial recipe,recipe for the targeted new grade, and the transition recipe), andprovide information to indicate the rationale for the transition productand the intended use (e.g., donated goods, internal corporate use,external customer, and the like). A human operator or supervisor may beprompted for approval to adapt a transition bill of materials. Theprompt may include automatically generated information about theproposed quantity of intermediate product to make and the intendedcustomer, the costs (including waste and delay) associated with making atransition product as opposed to a conventional direct grade change, andother information needed to make an informed decision regarding thegrade change.

Audit Logs

The PIPE system may include means for modifying PIPE data and auditmeans to document information regarding edited data. For example, autility known as PipeMap may allow an authorized user to view and modifydata. Data integrity may be maintained by requiring that anymodification be documented and justified with added comments, and bypreventing the deletion or modification of data that would disruptrelationships between tables and databases associated with the recordsbeing modified. When PIPE is adapted to multiple plants (manufacturingfacilities), an authorized user of PipeMap may access a list of plantsand, for each plant, a list of available tables. The plants may befurther categorized as a function of sector, business unit, division, orcorporation when the system spans the operations of more than onecorporate entity.

Tables that may be changed/updated may require an audit record to becreated and stored in an audit table in order to meet audit requirementsfrom external regulatory agencies (e.g., the Food and DrugAdministration of the United States) or general Good ManufacturingPractices. Through the audit log, changes to data values, date/time ofchange, and ID and name of person performing the changes may bedocumented and archived. These records may be viewed in the audit logwithin PipeMap and may allow the user to see if records have beenmodified or deleted. In addition the audit logs may apply a secure,computer generated, timestamp to the changed record and audit trail.

Database triggers may handle auditing of the various tables. Forexample, audit triggers may be SQL Server database table procedures thatexecute every time there is a specific action on a table, such as achange. They may be broken into multiple elements for each tablerequiring an audit trail. For example, there may be Update and Deletetriggers that will monitor the table to be audited for the respectiveaction. As modifications take place, the trigger may obtain all of therecord's previous information and add a transaction date and transactiontype to the audit table record. User comments may also be stored andtracked.

Where the data originates may dictate where the data may be changed. ThePipeMap utility may be adapted to allow map tables to only be changed atthe corporate level. PipeMap may also be adapted to allow plantproduction tables (tables of data from a particular plant) in the PIPEdatabase to only be changed at the plant level (by authorized plantpersonnel). Thus, a user viewing production data with the PipeMaputility may find certain fields are unchangeable.

The STORM system for tracking raw materials may follow differentstandards. Map tables (lookup tables) for material types, consumptionlocations, and so forth may best be editable at a sector or corporatelevel, while tables for materials, bill of materials (the recipe for aproduct), vendor locations, and so forth may be editable at a plantlevel.

Setpoint Management

Many products are produced with a wide range of attributes, such astissue with different colors, print patterns, topical additives, and soforth, or diapers having different sizes. A change in the grade for aproduct generally requires a variety of different process settings to beadjusted according to a recipe for that grade. Setpoint changes or othersetting changes may be tracked and recorded in an audit table that ispart of or linked to PIPE data such that the setpoints used for anyparticular production run may be associated with the products forsubsequent analysis or for providing documentation needed for regulatorycompliance. Software systems may be used to track and record currentsetpoints and to update recipes when new or experimental setpoints arefound to offer improvements.

Other features for managing machine settings may include the ability tomaintain multiple sets of settings. One set may be tentatively deemed asthe “best settings” for a grade. Current settings at any time may becaptured and compared to the “best settings,” and the current settingsmay be archived for later reference. When new settings are found to besuperior or required due to changes in raw materials or equipment, theymay be uploaded as the new “best settings” for that grade of product.Process control settings may be downloaded on demand, especially afterPLC replacement or software updates, when settings may need to berestored.

Use of PIPE with Other Software, Including Neural Networks/ExpertSystems

The PIPE system or other systems of the present invention may beintegrated with any suitable Human Manufacturing Interface (HMI),supervisory control and data acquisition (SCADA) system, or distributedcontrol system (DCS), including those provided by Wonderware Corp.(Irvine, Calif.), Rockwell International (Milwaukee, Wis.) and itssubsidiary, Allen-Bradley, Honeywell (Morristown, N.J.), HortonAutomation (Burnaby, British Columbia, Canada), and the like. Forexample, WONDERWARE brand manufacturing and process controloperator-machine interface software for plant operations or the RSView32SCADA/HMI package of Rockwell International may be integrated with PIPEand may send measured process parameters to the PIPE Event Logger, forexample, as well as be adapted to display reports derived from the PIPEdatabase for viewing by plant personnel or corporate personal via aclient server. The PIPE system or other systems of the present inventionmay be adapted to be part of or cooperate with the integratedmanufacturing system of U.S. Pat. No. 5,311,438, “IntegratedManufacturing System,” issued May 10, 1994 to Sellers et al.,incorporated herein by reference, or with any suitable related systems,including those of Rockwell International, Microsoft Corp., and othervendors.

The systems of the present invention may be incorporated into or linkedwith other suitable software systems such as electronic data interchange(EDI) systems and SAP brand software, or integrated with quality controlsystems and with computer-integrated manufacturing systems in general,including those described by J. Ashayeri et al. in “Computer-IntegratedManufacturing in the Chemical Industry,” Production & InventoryManagement Journal, vol. 37, no. 1, First Quarter 1996, pp. 52–57. Thesystems of the present invention may be integrated with SAP/R3 systems.Interfacing of custom software with SAP/R3 systems is described by B.Yeager in “Mead's ERP System Integrates ‘Best of Breed’ Software,”PIMA's North American Papermaker, vol. 82, no. 4, April 2000, p. 36. Forexample, encapsulation of custom software, such as any PIPE or STORMcomponent, may occur within SAP brand software using SAP brand softwareinterface programs, called “BAPIs” (business application programminginterfaces), which use Microsoft Corp.'s COM/DCOM connectors, allowing aMicrosoft-based client to communicate with SAP R/3. Such connectors maybe built using ACTIVEX brand technologies by Microsoft Corp. andCOM/DCOM strategies. For raw materials handling, suitable certificate ofanalysis generation tools may also be adapted, including the ProficyCertificate of Analysis Wizard, which is an ACTIVEX brand control. Otheraspects of applying a SAP brand software system for use with the presentinvention are disclosed by O. Wieser in “Integration of Process ControlSystems and Laboratory Information Systems Into the Logistic Chain,”Automatisierungstechnische Praxis, vol.39, no.2, February 1997, pp.26–28.

In one embodiment, a PIPE system (including STORM) may be integratedwith commercial quality control software, such as TRAQ-1 Quality DataManagement System of the Gibson-Graves Company, Inc. This software(compatible with VAX brand computers and peripheral apparatus) assistsin the management of quality assurance information. This system offersSPC (statistical process control) capability, as well as a range of dataentry, analysis, graphics and reporting features. It provides controlfor raw materials, process, and finished products. There are specificmodules for tracking and reporting of defective materials and returnedgoods, certificates of analysis, and vendor analysis. The system alsoprovides full database query and reporting capabilities. Graphicaloutput includes control charts, histograms, Pareto charts, cusum charts,x-y correlations, etc. DBQ (Database for Quality) brand computersoftware from Murphy Software may also be coupled with the systems ofthe present invention.

Another system that may be adapted for the present invention is theE-COMPLIANCE brand computer software system (hereafter TeC) of Taratec(Bridgewater, N.J.), as described in “Taratec Develops New Solution toHelp Life Sciences Industry Comply with FDA Regulation,” PR NEWSWIRE,Jan. 16, 2001. This system enables data and file management to becontrolled in a secure repository that supports the requirements of 21CFR Part 11. It allows security for all information to be maintainedthrough Access Control Lists (ACLs), which provide the flexibility togrant access as required while protecting files against accidentalmodification or unauthorized access. TeC also allows users withappropriate permission to update individual files while maintainingcopies of the original record and all subsequent versions. Secure audittrails capture information including date of modification, who modifiedthe file, and why the file was changed. TeC is said to be able tointegrate into most existing computer systems and is non-invasive todata sources or applications. Systems supported include laboratoryinstrument data collection applications, data entry applications, andelectronic batch records systems as well as MICROSOFT brand Excelspreadsheets and MICROSOFT brand Word files. Accessible through a webbrowser, TeC stores all files, from raw data to Certificates ofAnalysis, in a secure, central location with a full audit trail.Building on an Oracle8i platform (Oracle Corporation), TeC provides thesecurity and reliability of a Relational Database Management System(RDBMS) along with ease of use associated with standard file systems.

Vendor inventory management systems may be used, in which a request formore material is automatically generated as stores of the material aredepleted. Related concepts are described by C. Reilly, “Buyers toSuppliers: Manage My Inventory,” Purchasing, vol. 129, no. 1, Jul. 13,2000, p. 76 c. 39.

In one embodiment, a web-based version of the PIPE system (particularlyreporting features for PIPE) may incorporate XQuery, an XML querylanguage, as described by C. Babcock, “The Ask Master: An XML TechnologyMakes Retrieving Web Data Much Easier,” Interactive Week, Sep. 24, 2001,p. 48. An XQuery system, for example, may query a relational databasesuch as a product specifications database, as well as electronic dataprovided via web pages or e-mail, incorporating data from severalsources into a single XML document or web page. In another embodiment,Active Server Pages (ASP) may be used in cooperative relationship withan SQL server.

The rich and expansive body of process data obtainable with PIPE systemsis well suited for analysis and data mining by a variety of techniques,including neural networks/expert systems and data analysis employingfuzzy logic. For example, PIPE may be adapted for use with the neuralnetwork/expert system described in U.S. Pat. No. 5,121,467, “NeuralNetwork/Expert System Process Control System and Method,” issued Jun. 9,1992 to Skeirik, herein incorporated by reference, or in WO 00/20939,“Method and System for Monitoring and Controlling a ManufacturingSystem,” published Apr. 13, 2000 by J. D. Keeler et al., the U.S.equivalent of which was filed Oct. 6, 1998, and is herein incorporatedby reference. Other useful publications include the followingpublications: Tacker et al., “A Fuzzy Logic Neural System Approach toSignal Processing in Large Scale Decision System”, 1989 IEEE Conf. onSys. Man. & Cybernetics, pp. 1094–97; and Hillman, “Integrating NeuralNets and Expert Systems”, AI Exper., Jun. 1990, pp. 54–59. Such neuralnetworks and expert systems may play a role in the control of the systemusing rules learned by the neural network and in generating rules toguide the administration of the process (e.g., optimizing the use ofcrews, adjusting recipes to improve product yield or machineproductivity, etc.).

More particularly, neural networks and expert systems may be used toidentify factors that are associated with increased delay or lowermachine productivity in general. For example, a neural network mayanalyze PIPE data and determine that increased delay time from aparticular cause of delay is associated with the use of replacementparts from one of several vendors, with an interaction between aparticular crew and a particular product grade, or with certain rawmaterial properties in association with an attempt to increase machinespeed to an above average state. Identification of such factors may leadto creation of a new rule for production of the product, an alterationto the standard “recipe,” to eliminate the source of delay.

Optimization Using PIPE Data

PIPE data from multiple machines and mills may be analyzed to determinefactors that allow one machine or mill to have higher quality orproduction rates in order to suggest improvements for other operations.For example, mining of PIPE data may show that whenever Mill A uses rawmaterial from supplier B for product C, machine breaks are unusuallylow. A suggestion may then be generated asking a manager in Mill D toconsider testing raw material from supplier B for product C as well,giving a prediction of the expected reduction in down time. If such atrial is run, the results may be integrated with PIPE to validate orreject the hypothesis. The process of mining data to proposeimprovements for a given machine or for other machines may be done by aLearning Module, which may be directed by human input as to what typesof problems to investigate (e.g., finding optimum process conditions forselected grades or machines, finding synergy between machine componentsand process conditions, etc.).

PIPE data may be incorporated in optimization routines to determineoptimum scheduling of maintenance when a process problem is encountered.As with any other optimization procedure within the scope of the presentinvention, any known optimization strategy may be used. For example,Linear Programming (LP) may be used, as well as Mixed IntegerProgramming (MIP), genetic algorithms, neural networks, nonlinearprogramming (e.g., successive linear programming or distributedrecursion) and the like.

Integration of the PIPE system with maintenance scheduling systems mayallow maintenance schedules to be adjusted to reduce overall down time.For example, when a part failure occurs that has taken an average of 4hours to repair in the past, based on past PIPE data, and a 6-hour downtime is scheduled within a few days for routine maintenance, a messagemay be automatically-generated by a module in the integrated PIPE systemor mill control system to suggest that the future scheduled maintenancebe initiated immediately to reduce overall down time. An operator mayreview the suggestion and approve it, reject it, and optionally enterinstructions to increase the likelihood of similar suggestions or tosuppress the suggestion in identical future cases (perhaps because themaintenance may not be done when the failed part is inoperative). Thesystem may be programmed to search for accelerated maintenanceopportunities for any delay event or only for events of certain kinds orwith minimum expected down times. Scheduled maintenance events in adatabase may also be associated with maximum values for accelerated orpostponed execution (e.g., no more than a 2-day difference from thescheduled time) and with a minimum down time for which an accelerationin the schedule could be beneficial (e.g., since six hours are needed,don't combine with an unscheduled down time of less than three hours),or with other criteria.

As used herein, a “controller” or “control system” may refer to anyelectronic device or system of devices and any associated software forcontrolling a process to operate within specified limits. Controllersmay range in complexity from a simple control circuit to large,sophisticated systems such as a multi-facility distributed controlsystem employing Fieldbus Foundation-protocol field device systems andincluding central and remote installations with hardwired and wirelessconnections to numerous devices (see, for example, R. Bonadio and R.Argolo, “For Remote Stations, Fieldbus+PLC+Radio=Economical Network,”InTech Online, Feb. 1, 1999). Ethernet, Allen-Bradley and Rockwellnetworks are further examples of the many systems known in the art thatmay be used according to the present invention.

Control systems commonly employ PLCs (programmable logic controllers) totreat I/O discrete variables. The PLC central processing unit (CPU) notonly allows users to execute interlocking routines but also tocommunicate these internal variables for monitoring or actuationpurposes via, for example, EIA-232 or -485 ports. PLCs also have inputsfor analog variables such as current, voltage, and temperature sensors,as well as internal processing for arithmetic calculations andproportional-integral-derivative controls. Commonly, each I/O discreteinformation point requires one pair of wires connecting a field deviceto the PLC I/O module. For analog information, a transmitter orconverter is required to transform the physical variable (pressure,flow, pH, or level) to a standard signal (e.g., 4–20 mA). When there arecontrols in the system, it is necessary to have analog outputs to thevalve actuators.

The standard IEC 1131 defines hardware and software models as well asprogramming languages for programmable controllers that may be employedin the present invention. If desired, a neutral file exchange format(FXF) may be used as specified by PLCopen, based on a STEP (ISO 10303)model that allows transport of PLC programs from the programmingenvironment of one vendor to another. IEC 1131 languages are suitablenot only for PLC programming, but also as basis for a DistributedControl System (cf. IEC 1499). Other Fieldbus-related approaches toexchange of data include Device Descriptive Languages and Device DataBase (HART, FF, PROFIBUS). An international standard for the exchange ofproduct data, ISO 10303 (STEP) may also be used for integration ofvarious data types in a control system. See, for example, “TheEngineering of Distributed Control Systems” by René Simon.

ILLUSTRATIONS OF EXEMPLARY EMBODIMENTS

FIG. 1 depicts a PIPE-based manufacturing process 30 for a product 42that requires two stages, a first process 36 in the first facility 32Ato produce an intermediate product 38 from raw materials 34, and asecond process 40 in the second facility 32B to produce a final product42 from the intermediate product 38. The first facility 32A employs aPIPE system of the present invention to improve supervision of themanufacturing process. The first process 36 involves one or more unitoperations controlled by one or more operators 52A and desirablyoperated in a cooperative relationship with an automated process controlsystem 54A of any suitable kind. Events from the first process 36 arereceived and handled by a PIPE Event Logger 58, which may also timestampthe events based on clock 62 information.

The PIPE Event Logger 58 serves as a bridge between the process-relatedparameters (operator input, control system parameters, and other factorsobtained from or pertaining to the first process 36) and the PIPEdatabase 70. The PIPE Event Logger 58 may monitor event triggers (thougha separate program may be used for monitoring as well), may send alertsto appropriate personnel or devices depending on the nature of thealert, may format and may record event data in a PIPE database 70 and/orother database, may send a signal to the control system 54A of the firstprocess 36 indicating that a delay condition has been resolved and theprocess may be started up once again (e.g., sending permissiveinformation to a control system 54A to allow start up, ramp up, or otherprocedures following a delay or, in some embodiments, a delay and/orwaste event). The PIPE Event Logger 58 may also conduct or oversee errorhandling 64 or checking (data correction, for example), backup logging,and other issues. Backup logging may be especially important when a PIPEserver or communication line to the PIPE server is temporarily down, forthe PIPE Event Logger 58 may continue writing data to a text file (abackup log) or other file on a local computer until access to the PIPEserver is restored, at which time the backup log may be transmitted tothe PIPE server for entry into the PIPE database 70, preventing the lossof data that would otherwise occur.

Data entered in the PIPE database 70 regarding the intermediate product38 may be extracted to provide intermediate product data 74 for use inthe second process 40 at the second facility 32B. The intermediateproduct data 74 may be accessible via an intermediate productidentification code 60B, which may be associated with the intermediateproduct 38 with identification means (not shown) such as a bar code orsmart tag or other identification means. The intermediate productidentification code 60B may be read by a reader (not shown), by humanagents, or other means such that the code is entered into the controlsystem 54B (specifically, the interface module 82) for the secondprocess 40. Using the intermediate product identification code 60B, thecontrol system 54B may access the intermediate product data 74 from thePIPE database 70 (alternatively, the intermediate product identificationcode 60B may be sent directly to the control system 54B for the secondprocess 40 shortly before or as the intermediate product 38 is fed intothe second process 40). The operators may also view information from theintermediate product data 74, such as the product manufacturing history,the raw materials 34 used, the bill of materials used, manufacturingevents that occurred during production of the intermediate products 38,and measured material properties (not shown) which may also be linked toor entered into the PIPE database 70.

The PIPE Event Logger 58 may ensure that information pertaining to theraw materials 34 is stored and associated with the intermediate productdata 74 and data associated with the final product 42. In oneembodiment, the raw materials 34 are associated with smart tags, barcodes, or other identification means 44 which convey an identificationcode 60A that may be read by an identification reader 50 (e.g., ascanner for bar codes or an RF reader for smart tags). The readidentification code 60A may be fed to the PIPE Event Logger 58 andoptionally used to electronically look up information in a database (notshown) such as raw material properties, manufacturing information,electronic certificates of analysis, and the like. The identificationcode 60A, when archived in the PIPE database module 70, may serve as apointer to archived raw material data, or the raw material data may bearchived directly in the PIPE database 70.

The PIPE Event Logger 58 may use the read identification code 60A toverify that the raw materials 34 are suitable for the first process 36,the intermediate product 38, or the final product 42. This may be doneby comparing the data associated with the raw materials 34 with a billof materials (not shown) for the intermediate product 38 or finalproduct 42, or operators 52A, 52B may examine the data and verify thatit is suitable for the processes 36, 40 or products 38, 42. If the rawmaterials 34 are not suitable, the first process 36 may be shut downuntil the problem is resolved, or other alarms may be activated.

Data that are entered by operators 52A or that are obtained by othermeans may be checked for completeness or reasonableness with anerror-checking module 64. For example, error checking may involverequiring that entered numbers fall within a plausible range, or mayprevent entry of text when digits are needed, or may require that acomment field be filled before the entered data may be processes. Expertsystem rules may also be applied to check for common mistakes orunreasonable entries. In one embodiment, error checking occurs beforeany data are accepted by the PIPE Event Logger 58 or are written to thePIPE database 70.

In one optional embodiment shown in FIG. 1, the data added to the PIPEDatabase by the PIPE Event Logger 58 are checked for reasonableness orconsistency with a quality assurance module 66, and optionally correctedby the PipeMap utility 68 after review from human agents. If correctionsto the data must be made, the PipeMap utility 68 may properly correctthe data without damaging needed links between data in the database. Thequality assurance module may send a message by e-mail or other means(not shown) to a human to request intervention with the PipeMap utility68 to correct a possible problem. Any changes made or any responses tothe request for intervention may be logged and stored in anotherdatabase (not shown) for auditing purposes.

The PIPE database 70 is cooperatively associated with a financialreports system 56, such that data for a variety of financial reports areprovided from the PIPE database 70. Processing of data for financialreporting may be done after quality assurance procedures to ensure thatthe reported data are substantially accurate. In one embodiment, thePIPE-based manufacturing system 30 provides real time productivitymeasures for a machine which allows the profitability and yield of themachine to be updated daily in an intranet web server or other means.

The financial reports system 56 shown in FIG. 1 may include acorporate-wide reporting system that allows remote users to trackproduction, waste, delay, and/or profitability of a process, mill,sector, or other grouping of production operations, and to do so for anyperiod of time or preset time interval (e.g., current rates, cumulativeresults for any hour, day, week, month, year, etc., week to date, monthto date, year to date, etc.) and with the ability to display and printthe results by product grade or class, by machine type, by process, byproduction site, by sector, by customer, etc. For delay and wastereporting, reports may be provided for the top several (e.g., the top 5or top 10) most frequent or most costly (in terms of time andproduction) events or event categories. Waste and delay may be displayedor reported according to sections of the machine or machines of eitherfacility 32A, 32B (e.g., for a paper machine, the forming section, thepress section, the drying section, the calendering section, the coatingsection, the reel section, the slitting section, etc.), with reportsviewable by date, by grade, or by cross-direction region of the machinesin any section (e.g., by slitter position when multiple slitters areused, spaced apart in the cross-direction). Reports may be formatted astables, spreadsheets, bar charts, scatter plots, time series lines,hi-low plots, box plots, control charts, histograms, Pareto charts(e.g., a bar chart showing percent defects as a function of defect type,in descending order), cause-and-effect charts, Ishikawa diagrams (alsoknown as fish bone diagram), various three-dimensional plots, compositecharts, interactive charts such as manually rotatable scatter plots inthree or more dimensions, multimedia presentations with animation toshow changes in times (e.g., Flash files), JPEG movies, DHTML or XMLpages for interactivity in a web browser environment, and the like.

The PIPE database 70 also may be cooperatively associated with a workorders and maintenance module 72, which may automatically generate workorders and schedule maintenance based on events recorded in the PIPEdatabase 70 (or directly based on events detected by the PIPE EventLogger 58). For example, an increased incidence of registration problemsin a machine for assembling components of a diaper may be known to beassociated with degradation of certain pieces of equipment. An expertsystem within the work orders and maintenance module 72 may detect theincreased incidence of registration problems and deduce that maintenanceof the responsible equipment is needed. The maintenance may beautomatically scheduled, optionally subject to approval from anauthorizer who may manually review the data or justification for thework, optionally supported by a justification report created by the workorders and maintenance module 72, which may include a bar graph showingthe trend of increased registration problems. Also by way of example, apart failure entered as a PIPE event in the PIPE database 70 may benoted by the work orders and maintenance module 72 which may thenautomatically issue a work order for repair and create a purchase orderfor the needed materials. Projected cost of the work and parts may beentered into the financial reports system 56.

The intermediate product 38 from the first facility 32A is submitted tothe second facility 32B as a raw material in a second process 40 toproduce a final product 42. The second facility 32B is not shown asequipped with a PIPE system, though it may be. But the second facility32B is assisted in its operation by the intermediate product data 74supplied by the PIPE system of the first facility 32A, which provides adescription of the events that occurred during production of theintermediate product 38 and optionally other process control andmaterial property information, such that the second process 40 at thesecond facility 32B may be adjusted with a control system 54B adaptedfor feed forward control, or the process may be modified by manualcontrol or other means responsive to the intermediate product data 74 tobest utilize the intermediate product 38, particularly by anticipatingdesired modifications in process conditions responsive to theintermediate product data 74. Intermediate product data 74 from the PIPEsystem may be supplied to both operators 52B and the control system 54Bof the second process of the second facility 32B for both human-guidedand automatic control adjustments based on the attributes of theintermediate product 38 or the defects to be encountered during its use.Feeding PIPE information (event-based production data) obtained from theproduction of a raw material 34 or intermediate material 38 into theproduction system for a final product (or other intermediate product) isone aspect of certain embodiments of the present invention directedtoward Intelligent Manufacturing.

In one embodiment, the control system 54B of the second process of thesecond facility 32B comprises an interface module 82 and a controlmodule 84. The interface module 84 receives the intermediateidentification code 60B associated with the intermediate product 38 viaa second ID reader (not shown) or manual entry of the intermediateidentification code 60B. The control module 84 may then access theintermediate product data 74 associated with the received intermediateidentification code 60B from the PIPE database 70 or other repository ofthe intermediate product information. The interface module 84 maycompare the intermediate product data 74 with the specifications of abill of materials from a bill of materials (BOM) database 100 to verifythat the intermediate product is suitable for the second process 40 andfor the requirements of the final product 42.

The control module 84 direct process control for implementing processmodifications in response to the obtained data, making the manufacturingsystem 30 a feed-forward manufacturing system, regardless of thehardware and software details of the local process control systems 54A,54B. Process modification for the second process may include adjustingat least one setting of a machine in the second facility 32B or at leastone other ingredient in the recipe provided by the bill of materialsdatabase 100. For example, if the intermediate product is a tissue webwith lower than normal strength, the amount of an adhesive reinforcementapplied to the tissue in the second process may be increased to stillprovide an acceptable final product 42. The bill of materials mayprovide acceptable ranges and provide instructions to compensate forcertain ranges of material properties of the intermediate product orother raw materials, such as increasing an applied adhesive when amaterial property is lower than the target value but still acceptable.The final product 42 may then be associated with a final productidentification code (not shown) which points to archived data from thefirst and second processes 36, 40. The final product 42 may later serveas a raw material for yet another process, or may be used commerciallyin any way.

FIG. 2 depicts another embodiment of a manufacturing process 30 similarto the PIPE-based system for the first facility 32A in FIG. 1, but withthe bill of materials 102 in a bill of materials database 100 beingexplicitly shown. The bill of materials 102 is accessed by the controlsystem 54 and is compared with the properties of incoming raw materials34, identified by accessing a raw material database (not shown) or byobtaining information from other sources that is associated with theidentification code 60 for the raw materials 34 obtained when anidentification reader 50 reads identification means 44 on or associatedwith the raw materials 34. The bill of materials 102 may containnumerous fields associated with the process 36 and its intended product(not shown). Such fields may specify the recipe (approved materials,process conditions, amounts of materials to use, etc.) for the process36, as well as the target properties 106 desired for one or more rawmaterials 34 that may have varying material properties. In addition tospecific values of target properties 106, acceptable ranges ofproperties may be specified in the acceptable range field 108 for one ormore materials. Since properties away from the target values but stillwithin the acceptable range may sometimes require adjustments in processconditions or in the amount or selection of other materials, anadjustments field or fields 110 may provide instructions for desiredprocess modifications to compensate for off-target properties of the rawmaterials 34 or for other temporary limitations in the process 36.

Before the raw materials 34 are accepted into the process 36, the rawmaterials must be accepted in a process entry stage 90 in which thecontrol system 54 directs acceptance or rejection of the raw materials34 according to compliance of the raw materials with the bill ofmaterials 102. If the raw materials 34 are inappropriate or havematerial properties outside the acceptable range, the raw materials 34may be rejected as rejected materials 92, which may then be recycled,returned to the vendor, warehoused for later processing, or the like.Raw materials 34 accepted for process entry 90 may then be used in theprocess 36, which is controlled by the control system 54, and which mayprovide event data and other information sent to the PIPE Event Logger58 for storage in the PIPE database 70.

FIG. 3 depicts a flowchart showing several of the steps involved in aPIPE system 31 that is used for financial reporting. Process sensors 46associated with a process (not shown) provide data that allow the systemto monitor events 86 related to productivity. When a trigger event isdetected 88 (an event requiring event data to be entered in the PIPEdatabase 70), process variables are obtained 94 from the control system54, along with obtaining operator input 96, specifically human input 76from an operator who is queried by a display screen (not shown) toprovide information needed in the PIPE database 70. Before (or,alternatively, after or as) the data are entered, the data are checkedfor completeness or obvious errors (incorrect ranges entered, etc.) withan error-handling step 78, which may be substantially limited to basicchecks on values entered. The data may then be properly formatted andrecorded in the PIPE database 70. Before financial reporting isexecuted, the database should be checked for quality with a data qualityassurance module 114 which may be substantially more comprehensive thanthe error handling step 78 in that entries from multiple periods of timeand multiple fields of data may be evaluated for consistency andreasonableness. Operator input in particular may need to be validateddue to the possibility of human error (e.g., wrong error code, failureto enter a description of the cause of delay, etc.). For example, if anoperator indicated that the machine delay was due to a plugged glueapplicator but machine records show that a work order was issued andfilled to replace defective roller bearings during the delay,verification of the cause of the delay may be needed and requested, withcorrection of possible errors handled with the PipeMap utility 68. Ingeneral, a data quality assurance module 114 may assess the validity ofthe data and prompt the operator or others to correct obvious problemsor confirm questionable entries. Though this is shown as occurring afterdata are recorded in the PIPE database 70, this operation may be appliedto at least some of the data prior to entry in the PIPE database 70.

Validated data may then be processed to yield reports, and inparticular, may be used to generate financial reports 116. While this isshown as occurring after data validation 98 has occurred, choosing togenerate reports on non-validated data is not outside the scope of thepresent invention, though for best results the data should be validatedto at least some degree to ensure that spurious waste and delay entriesdo not distort results. To the extent that human and machine errors maybe substantially eliminated, the need for a quality assurance module 114may be correspondingly reduced.

Report generation may proceed by the use of a query 118 from a client toextract selected data from the PIPE database 70 and other databases (notshown) that are accessible via links using pointers in the PIPE database70, e.g., a pointer to a STORM database (not shown) to provide rawmaterial information, for showing a table comparing diaper waste forsimilar diapers made with mechanical fasteners from two differentvendors, or a pointer may point to a Bill of Materials database (notshown) to permit sorting of delay by details of the recipes used toproduce certain products, for comparing the waste when either of twodifferent airlaid materials are used in the production of an absorbentmedical article, or a pointer to a Shift database indicating which crewwas used during the production of a product, so that a report maycompare the waste and delay results experienced with two or more crews).Queried results may be treated with filters (not shown) in a variety ofways to segregate data, such as segregating delay results according tothe slitter position in which the web component of a medical article wasslit prior to being used as a raw material.

PIPE data may then be incorporated into a display or hardcopy of afinancial report 120, such as a table of waste and delay for multipleplants during a week or month, or most common causes of delay for aproduct category before and after a change in the recipe for a product.The productivity data may be entered into a financial database (notshown), where it may be rolled up for later use in, for example, anannual report.

For example, Table 4 shows a record of delay history for a material lotreturned in response to a query. In one embodiment, the query returns asmany delay records as exist in the database. In this example, there isonly one record.

TABLE 4 Record of Delay History. Delay Machine Event Delay LocationMachine Delay Ref. Duration (yards) Delay Code Section Problem Machine 129:22.0 913 481 Bonder Main Air Not on Fault

In another example, Table 5 shows a record of splices for a material lotreturned in response to a query. In one embodiment, the query returns asmany delay records as exist in the database. In this example, there isonly one record.

TABLE 5 Record of Splices. Machine Roll Splice Location Total Ref.Identifier (yards) Length Machine 1 Roll 1 238 1916

In one embodiment, the PIPE system automatically prepares a MICROSOFTbrand Excel spreadsheet with the productivity from one or more machinesto be incorporated in the step of generating financial reports 116 ordisplayed on an electronic display 120 such as an intranet web page formanagement review.

FIG. 4 is a hypothetical plot 122 of machine speed versus time toillustrate an exemplary definition of delay during a series of eventsrelating to machine productivity over time. The vertical axis 126,machine speed, shows the rate of operation of a process relative totime, shown as the horizontal axis 124. Initially at nearly full speed,the machine encounters a trigger at a particular time to end the run.The trigger may be due to a machine-detected web break, an operatorinput due to a safety concern, a lack of suitable raw materials forprocessing, a machine error, or other cause. The trigger initiates amachine shut down. The machine decelerates to zero speed. In onedefinition, the delay time only begins when the machine is atsubstantially zero speed, and ends when the machine begins moving again.This is the definition used to mark delay in FIG. 4. In an alternativedefinition (not shown), the delay time may be defined to span the timefrom the trigger to end the run state (or from the time when the machinehas decelerated to a predetermined speed after the trigger) until themachine begins moving again.

After the cause of the delay has been overcome, the machine is startedagain to resume production. Typically, for an operation with one or morewebs of material, a period of low-speed operation is needed to ensurethat components are properly aligned and pass through the system in thecorrect manner (threading). The time for threading generally needs notbe counted as delay, though one may employ a definition of delay inwhich operating below a threshold speed is considered delay. Aconsistent definition of delay for reporting is generally more importantthan which reasonable definition is selected. After successfulthreading, the machine speed is ramped up to normal running conditionsagain.

FIG. 5 is a bar chart 132 showing how the maximum capacity 134 of amachine may be distributed. Specifically, the bar chart 132 depicts waysin which the realized capacity (productivity or production rate) of amachine may be less than the maximum capacity 134. In the first column130A starting with the left, a strategic portion 136 of the maximumcapacity 134 is deliberately allocated for modes that do not result inproduction, such as scheduled downtime 138 for maintenance or otherpurposes, including holidays, inadequate demand or manpower (marketcurtailment), research runs that do not result in shipped product, etc.Part of the strategic portion is due to operation at a lower than idealspeed (the “speed loss vs. ideal” 140 part of the strategic portion136), perhaps to maintain a certain quality standard or to conserve anexpensive component more likely to fail at full speed. The resultingscheduled capacity 142 is thus lower than the maximum capacity 134.

In the second column 130B from the left, delay time 144 decreasesproductivity. Delay time 144 may be due to changes in grades, machineproblems, and so forth. The remaining portion of the time scheduled forproduction is the uptime 146. In the third column 130C from the left,the productivity realized during uptime 146 may be lowered by running ata speed lower than what has been targeted (the planned speed, whichhelps determine the scheduled capacity), causing delay due to the speedloss versus target speed 148. Thus, speed loss decreases the capacitythat could have been achieved within the available uptime 146. Theactual speed of the machine during uptime gives a gross production ratetermed the “system rate” 150.

As illustrated in the last column 130D, the actual product yield 154generally will be lower than may be realized at the system rate 150 dueto machine waste 152 (e.g., product that must be discarded). Thus, therealized capacity 158 is less than the scheduled capacity 142 due to areliability loss 156, as shown.

In several embodiments of the present invention, the PIPE system tracksdelay, speed loss 148, machine waste 152, scheduled capacity 142, andstrategic reductions 136 from maximum capacity 134, allowing regular oreven essentially real-time reporting of the various sources of lostcapacity, and optionally displaying the financial cost of such losses.Reports may be provided in any desired format, such as tabular,text-based, or graphical formats, and may be online reports, printedreports, and so forth.

FIG. 6 depicts an embodiment of a PIPE-assisted manufacturing process 30in which raw materials 34 are converted by a process 36 using a machine48 to yield a product. As used herein, “machine” may refer to allequipment and unit operations used to convert raw materials 34 to aproduct 42, or to a subset of the equipment and unit operations neededto produce the product 42. Multiple sensors 46 (boxes labeled with “S”)detect process conditions and other variables pertaining to the machine48 and the process 36 of converting raw materials 36 to the product 42.These sensors 46 may be read by the PIPE Event Logger 58, the controller176 of a process control system (e.g., a system governed by WONDERWAREbrand manufacturing and process control operator-machine interfacesoftware, including those incorporating the FACTORYSUITE brandmanufacturing and process control software of Wonderware Corp.), orboth. Sensor data read by the PIPE Event Logger 58 may be forwarded tothe controller 176, where well-known principles of process control areemployed to control the system by any suitable means, including the useof actuators 178 (the box labeled “A”) to modify one or more aspects ofthe process 36. The PIPE Event Logger 58 obtains process data and adds atimestamp from a clock 62 and also provides a means for operator input76 to more fully describe an event or to specify the apparent nature andcauses of the event. Operator input 76 may be received through acomputer or any other suitable human-machine interface (HMI) (notshown). The acquired data from the PIPE Event Logger 58 is thenforwarded to a PIPE Database 70, where it may be used for generatingfinancial reports 56, desirably after the data have been examined forintegrity using a Data Quality Assurance module 160. Problems that maybe checked include apparent typographical errors, incorrect machinestate codes, delay or waste values that seem inordinately large, and soforth.

The financial reports 56 generated with the corrected PIPE data may bein any suitable format, such as web page 162 on a secure Internet siteor an Intranet to allow remote employees to observe productivity, waste,delay, other desired parameters, including lost profit, cost of wasteand delay, performance relative to targeted Key Performance Indicators,and so forth. In one embodiment, a web page 162 continually providesreal time productivity information in a format customizable by the userso that machine or plant operation may be tracked essentially in realtime, or by certain units of time such as hourly, by shift, per day,weekly, monthly, and so forth. Printed publications 164 may also beprepared in any form. In another embodiment, a spreadsheet 166 such as aMICROSOFT brand Excel spreadsheet or other spreadsheet tool is preparedin a format that may directly be incorporated into a report, such as amonthly report, quarterly report, or annual report.

FIG. 6 further depicts a neural network 168 that mines data from thePIPE Database 70 to look for relationships that account for waste anddelay, or for combinations of process variables or other factorsassociated with decreased waste and delay, in order to propose rulesthat may be tested for their ability to improve process productivity.For example, the neural network 168, as governed by the neural networkconfiguration 172, may operate on historical data and identify arelationship between the winter season and periodic delay associatedwith inadequate inventories of raw materials 34. The findings regardingthe seasonal increase in delay due to raw material problems may then beused to propose seasonal adjustments to managing raw materials, and maybe used to track the root causes of the problem, such as a purchasingagent who tends to be ill more in winter months. In one embodiment, theneural network 168 may have access to employee absence information forpossible correlation with certain aspects of productivity. Correctiverules may then be suggested by a process administration entity 170,either automatically or by a process administrator (e.g., a plantmanager) to whom the findings of the neural network 168 have beencommunicated. Improved rules 174 for administering raw materialpurchases and inventories may then be promulgated by the processadministration entity 170, in response to the neural network 168findings. The process administration entity 170 may also change anynumber of factors pertaining to the system, such as crew composition,policies for grade changes, machine settings (in cooperation withoperators), and so forth.

The neural network 168 (or a second neural network, not shown) may alsobe used to identify optimum process variables to reduce delay, and theserecommended optimum variables may then be communicated to the controller176 of the control system (or to a human operator or processadministration entity 170) to improve performance and reduce waste anddelay in the process 36.

The success of any changes made to the process 36 or its manner ofoperation may also be examined with the financial reports 56 generatedin association with the PIPE Database 70. For example, before and aftertrends in waste or other parameters may be displayed graphically or indata tables to show the apparent impact of change made.

FIG. 7 depicts another embodiment of PIPE-assisted manufacturing process30 in which raw materials 34 are converted by a machine 48 in a process36 to yield a product. The operation is similar to that of FIG. 6 exceptthat an embodiment of the quality assurance module 160 is shown in moredetail. Here the dotted box labeled “PIPE Data Quality Assurance” 160shows that PIPE data from the PIPE database 70 are submitted to a DataIntegrity Agent 180, a step that should occur before any PIPE data areincorporated into financial reports 56, or at least before the data areincorporated into permanent financial reports or other subsets offinancial reports such as those made available to the public. The DataIntegrity Agent 180 is an intelligent agent cooperatively associatedwith fuzzy expert rules 188 (or any other artificial intelligence systemor system of rules governing the integrity of PIPE data). The fuzzyexpert rules 188 may, in one embodiment, be continually updated orrefined through the learning of the neural network 168 that mines PIPEdata and, in cooperation with a process administration entity 170 (suchas a human administrator or a supervisory artificial intelligenceprogram), yields recommended rules 174 for improved operation of theprocess 36 to increase productivity or quality. Some of the rules, asshown with the dotted line 190 from “Rules” to “Fuzzy Expert Rules,” maybe used to flag anomalous or suspect data.

The Data Integrity Agent 180 examines data and looks for anomalies,discrepancies, errors, including conditions that are a specified numberof standard deviations away from the expected value or outside thenormal extremes for the process 36. For example, if 6 hours of down timeare attributed to the need to clean a meltblowing nozzle, a flag may beraised for that entry in the PIPE database 70. The flagged condition isrecorded 182, and an intervention request 184 is generated by the DataIntegrity Agent 180, which may be e-mail sent to an operator or plantmanager, a copy of which may be archived in an audit database (notshown). In response to the intervention request 184, modifications 186of operator-entered data (or other data, if needed) may be made and thecorrections logged and stored in the audit database (not shown). Afterthe integrity of the data has been checked, the corrected PIPE database70 may then be used for the generation of financial reports 56 or otherreports (GMP reports, etc.). For best results, the financial reports 56should only be generated after a check of data integrity has occurred,whether the check is done repeatedly and automatically by a DataIntegrity Agent 180 or in other ways, such as by other integritychecking means in response to a request for a financial report 56.

The Data Integrity Agent 180 may also perform simple checks such asconfirming that each day has 24 hours of accounted time (e.g., the totalof time distributed between mutually exclusive categories such as runtime, down time, unscheduled time, and so forth).

Though not shown in FIG. 7, an additional expert system or neuralnetwork may be employed to learn from the modifications that are made byhuman users to the PIPE data in response to the intervention request184. Comparing flagged records 182 with the resulting modifications 186may be used to update the rules used by the Data Integrity Agent 188 aswell as the recommendations made in the intervention request 184.

FIG. 8 is a flow chart showing steps preceding the shipment of a rawmaterial from a vendor of the raw material to a manufacturer for use inmaking a product. The production of the raw material by the vendor isdone in association with the manufacturer's use of a PIPE system 212.The flow chart begins with the vendor receiving a unique vendor ID 196from the manufacturer that may form a portion of the license plate codeused to track a raw material. The vendor code is supplied by themanufacturer and is entered into the manufacturer's STORM/PIPE system212. The vendor, already having a vendor ID code, may then receive anorder 198 for the raw material from the purchasing department 210 of themanufacturer. The vendor may then produce the raw material 200, and thencreate a batch code 202 for the raw material which may be coupled withthe vendor ID code to create a unique code identifying the raw materialand allowing tracking of the raw material and its material properties,which are measured 204 and entered into a materials database 190 withthe license plate code (including the batch code+vendor ID code).

In a step not shown in FIG. 8, an electronic certificate of analysis maybe created to allow the manufacturer upon receipt of the raw material toverify that it is within specifications and suitable for use for aparticular product, using the Supplier Data Management System ofcommonly owned U.S. patent application Ser. No. 10/253,200, “SupplierData Management System,” filed Sep. 23, 2002 by Amy H. Boyd et al.,previously incorporated by reference.

After measurement of the raw material properties 204, the raw materialmay be provided with identification means 206 (a bar code, smart tag, orother means) to convey the license plate code. The raw material is thenshipped to the manufacturer 208, who may electronically read theidentification means upon receipt to identify the raw material and gainaccess to the associated data in the materials database 190 to examineany raw material properties, comments, or other information pertainingto the raw material prior to using the raw material in the manufacturerof a product.

FIG. 9 depicts one embodiment a PIPE-assisted manufacturing process 30showing a hardware configuration according to the present invention. Thefigure shows three levels, the machine level 216, the plant operationslevel 218, and the remote location/corporate enterprise level 220. Atthe machine level 216, a machine network 222 including PLC devices 236or other sensor systems provides data to an HMI server 224 equipped withcontrol software such as WONDERWARE brand manufacturing and processcontrol operator-machine interface software (not shown). The HMI server224 may be interfaced with or connected to other operator stations(e.g., a WINDOWS NT brand operating system operator station) as well,depicted as the HMI client 226. An event logger 228 program shown ashosted on the HMI server (though it could be on another connected devicein a LAN), runs to monitor events based on information obtained from themachine network 222 by PLC devices 236 or sensors in general. The eventlogger 228 sends event data to the PIPE server 230 via a machine switch232 and plant switch 234, or, if the PIPE server 230 is down or thereare other communication problems, the event logger 228 creates a backuplog 252 of event information that may be in the form of SQL statements.The HMI server 224, in general, sends operations data out to the PIPEserver 230, and may receive master table information from the PIPEserver 230 as well to govern the format or form of data sent out. Datasent to the PIPE server 230 may be archived in the plant PIPE database256.

Manual delay data and information from WONDERWARE brand manufacturingand other process control operator-machine interface software may beshared directly between the HMI server 224 and the HMI client 226 (orother WINDOWS NT brand operating system operator stations). A machinelogger 238 running on the HMI client 226 (or it may be elsewhere, suchas on the HMI server 224) responds to events detected by the eventlogger 228 and presents an alert to operators, requesting comments andother information to characterize the delay. Delay information iscaptured by the machine logger 238 and forwarded to the PIPE server 230via the machine switch 232 and plant switch 234. The machine switch 232also may receive other information 250 from various unit operations (notshown) and raw materials information 240, such as scanned license plateinformation or general bar code or smart tag information scanned by awireless scanner 242 and transmitted via radio frequency signals 244 toa receiver 246, which sends the transmission to the machine switch 232.From the machine switch 232, the raw materials information 240 may besent to a STORM server 254 (e.g., a WINDOWS NT brand operating systemserver) via the plant switch 234 where related databases may beaccessed, or raw materials data may be directly loaded into the STORMserver 254. Raw material information 240 from the STORM server 254 maybe checked against a bill of materials database 254 to verify that theraw material is suitable for the product to be made. An electroniccertificate of analysis (not shown) may also be generated or madeavailable and transmitted back to the HMI server 224 or other monitoravailable to operators performing the scan 248 of the raw material.

Several functions may be executed either by the event logger 228 orsoftware on the STORM server 254, as desired. The STORM server 254 maybe the host computer for raw material scanning 248, and may drive dumbterminals or other displays to guide operators receiving or loading rawmaterials, including generating a signal to generate a display inresponse to a scan showing the acceptability of a raw material ordisplaying the certificate of analysis for the raw material. Thedecision to accept a raw material may be made by the STORM server 254,responsive to bill of material information obtained from the BOMdatabase 254 or other information from the plant database 256. The STORMserver 254 may send a signal to an operator and/or to the event logger228 when an incorrect material is being loaded or considered for loadingto warn of the problem and optionally to shut down the machine orprocess until correct raw materials are provided, or until an overrideis authorized or a recipe is altered. Information is shared with thePIPE server 230 and may be written to the plant PIPE database 256 or apointer may be added to the plant PIPE database 256 to show the locationof the raw materials data on the STORM server 254.

At the plant level 218, one or more computers for plant access 264 allowemployees to access HMI systems or other control systems and plantreporting systems (not shown), and to access PIPE information and PIPEreports via the PIPE server 230 and its connection to the plant PIPEdatabase 256.

A router 258 joins remote locations and corporate-level 220 systems tothe plant level 218 PIPE server 230 and associated databases 254, 256,helping to provide means for communication of information 260 betweenthe plant level 218 and corporate level 220. Computers providing remoteaccess 266 at the corporate enterprise level 220 are connected not onlyto the plant level 218 PIPE system, but also to an enterprise SQL serverfor PIPE rollup 268, in which the PIPE databases of multiple plants (notshown) are periodically rolled up at the corporate level 220 byproviding data for the central PIPE database 270, accessed via theenterprise SQL server 268 for PIPE rollup. A financial reporting server274 with access to a financial or other corporate database 272 is alsoconnected to the enterprise SQL server 268 via the router 258, allowingfinancial analyses and forecasts to be made on the basis of current andhistorical productivity levels. Numerous charts and tables may begenerated, and many aspects of plant, sector, and corporate performancemay be analyzed through the availability of rolled up PIPE data andconsolidated operations data on the enterprise SQL server 268.

The central PIPE database 270 may host standardized maps describing howPIPE data from the plants is formatted or interpreted, and these mapsmay be downloaded to the plant-level PIPE servers 230 as needed via theenterprise SQL server for PIPE rollup 268. The plant-level 218 PIPEdatabases 230 may host PIPE support tables, PIPE output tables,packaging tables, and STORM tables, for example. Mapping information andoperational information may be shared between the plant-level PIPEdatabase 256 and the PIPE server 230.

FIG. 10A and FIG. 10B show how the product variables and processvariables may be loaded, saved, and updated for a manufacturing process30 incorporating a PIPE system of the present invention. FIG. 10Adepicts a local plant database 276 that maintains records for productvariables 278 including product recipes 280 or product specifications(e.g., the composition, size, color, etc. of various product grades) andsettings for process variables 282, which may be linked to or may beindependent of the grade recipes. In the four steps shown 286, 288, 290,and 292, an operator first chooses to change grades, switching thecurrent recipe 280 from the recipe for Grade A 280A to the recipe forGrade C 280C. The HMI server 224 then sees the grade change and saves294 the current process variables by writing them into the stored recipefor Grade A 280A (this may be an update of a previous recipe for GradeA). The Grade C recipe 280C is then loaded into the HMI server 224,which in turn updates the control processors for the machine network 226via communication means 294 which may include an Ethernet system, aDCSNet system, and the like. A central or plant PIPE database 70periodically records 310 the product variables 278 and process variables282, taking “snapshots” of variables in use at particular times. Thesevariables may be accessed to identify the operating conditions when aparticular product was made.

In one embodiment, each recipe 280 comprises product specificationfields 304 including a name field 312 and at least one data field suchas data field #1 314A through data field #N 314N, where N is a positiveinteger.

The records in the PIPE database 70 may include delay records 306 foreach event comprising data fields such as a delay code 316A, a delayduration 316B, a time stamp 316C indicating when the event occurred, anda product count 316D (e.g., the number of articles that had beenproduced in the shift or production run prior to the event). The PIPEdatabase 70 may also include waste records 308 for waste events. A wasterecord 308 may include data fields such as a waste code 318A, a defectcount 318B (e.g., the number of articles lost), a time stamp 318C, aproduct count 318D, and so forth. Other fields (not shown) may indicatewhere in the machine a defect occurred (if this information is notalready uniquely indicated by the waste code 318A or delay code 316A),which operators were active, etc., and may provide links to the currentrecipe 280 and current process variables 284 that were in use when theevent occurred.

FIG. 10B illustrates a manufacturing process 30 related to that of FIG.10A, showing the events that occur when a change is made in the processvariables 282. First, a process variable change on the machine (notshown) is detected 322 or reported to the HMI server 224. The HMI server224 then accesses the plant database 276 and updates 324 the table ofprocess variables corresponding to the current variables 284. The newvariables, if deemed to be superior to previous variables for a grade,may be recorded as the specified variables for the corresponding gradeor grades. For example, Variable Set 1 284A may be suitable for use withthe recipes for Grade A 280A and Grade D 280D, and may be the variableset that is in force as the current variables 284. After examination ofPIPE data, an operator may determine that runnability would be enhancedon the average by adjusting a process variable such as a temperature ina reactor. After the changed variable is detected 322 and used to updatethe current variable set 284, the operator or process administrator mayuse the new current variable set to redefine Variable Set 1 284A, or tocreate a new variable set (not shown).

The recorded process variables 282 may then be used again later, such asbeing downloaded to the HMI server 224 from the plant database 276 torestore 320 the process variables 282 when new HMI software isinstalled, when new control applications are installed, following a shutdown, or when a grade is changed. In some embodiments, process variablesare present in the HMI server for routine grade changes and are onlydownloaded from the plant database 276 when there is a need to restore320 process variables.

FIG. 11 and FIG. 12 depict audit operations for modifying or deletingdata from a PIPE database. In FIG. 11, once a need has been recognizedfor record modification, the process begins with a message being sent tomodify a record. The message results in a modification request beingrecorded in a source table, and a copy of the original unmodified recordbeing stored in an audit table. The modification is then made, changingthe record the source table. FIG. 12 shows that a similar process isapplied when deleting records, resulting in the request to delete arecord being stored in a source table, while the original record isstored in an audit table, followed by executing the command to delete arecord from a source table.

FIG. 13 is an exemplary screenshot for a reporting system developed foruse with a PIPE system of the present invention. Shown are the types offilters and query information that the user may select to obtain chartsor tables of productivity information and other financial reports basedon a reporting system cooperatively associated with the PIPE system ofthe present invention. Exemplary reporting parameters may include meantime between shutdowns, mean delay on shutdown, occurrences of waste ordelay events, and so forth. As shown, reporting may be generated for anentire plant, a specific machine, a section or subsection of themachine, a particular crew, or a particularly product or group ofproducts. Reports may explore productivity issues for particular eventtypes or production modes (e.g., normal, start ups, research runs, etc.)or machine states (start up, threading, accelerating, full speed, etc.).Once selections have been made, results may be displayed as a table, achart, a graphical depiction of the process with embedded or hyperlinkedreport details, etc.

The reporting system processes data stored in a database. The dataincludes, but is not limited to, automatically collected event-basedwaste and delay records in a manufacturing system. Each recordrepresents an event and includes, for example, a timestamp, an eventcode, and a measure of cost or production loss associated with theevent. The reporting system may be implemented as a system on one ormore computer-readable media having computer-executable components.Further, the system may include a graphical user interface including adisplay and a user interface selection device. A display device rendersthe display.

In general, the reporting system displays a report user interface asillustrated in FIG. 13 via a view component on the display device to auser. The user interface defines a plurality of time periods and aplurality of financial report types. The reporting system receives fromthe user via the user interface selection device a selected time periodcorresponding to one or more of the time periods and a selected reporttype corresponding to one or more of the report types (e.g., via aninput component). With reference to FIG. 3, in response to the receivedselections (e.g., as query 118), the reporting system retrieves reportdata associated with the selected report type for the selected timeperiod from the data in the database in response to a user command(e.g., via an execution component) to generate a financial report at116. The retrieved report data pertains only to the selected timeperiod; that is, data from any other time period is excluded from thereport data. The time periods include, but are not limited to, amonth-to-date time period, a week-to-date time period, a year-to-datetime period, a last thirty days time period, a last seven days timeperiod, and a user-specifiable time period. For the user-specifiabletime period, the analyst specifies a start date and an end date. In theembodiment of FIG. 13, the user may use the user interface selectiondevice (e.g., a mouse) to select the “SUBMIT” button to issue the usercommand or send an execution signal to the reporting system.

In addition, the report user interface allows an analyst to trackinformation such as percent of rolls with a delay event, spliceefficiency, and the waste and delay associated with different unwindspindles, raw material slit position, lot number, and position within aroll. The analyst may also track, for example, the amount of waste ordelay as a function of the raw material age to allow the analyst tooptimize inventory levels. In addition, the PIPE reporting page allowsan analyst to “drill down” through data. In other words, the analystusing the report user interface can roll up at the sector level anddrill down by mill (e.g., selecting a mill). After clicking a mill, theanalyst may further drill down by machine. Further, the analyst mayclick a sector delay number or waste number and to generate a reportthat shows top delay items or waste items by mill. These numbers may befollowed to drill down to the machine level. The report summarizes wasteand delay by machine section and/or a “top 10” or “top 50” report of thehighest causes of waste and delay.

EXAMPLE 1

A raw material change was implemented across a number of productionfacilities, resulting in direct cost savings over the previous material.There was a strong perception among employees at the production levelthat this new material caused a number of processing problems and had adeleterious effect on machine uptime. Increased process interruptionsand machine downtime may potentially negate the savings achieved withthe raw material change.

Anecdotal evidence from production facilities caused concern thatoverall cost savings might not be what was expected because of theincreased processing difficulties. Although it seemed clear that somefacilities were experiencing difficulties with the new material, a totalcost savings analysis was difficult because different machines changedto the material at different times. Further, the facilities tended touse different criteria for determining the impact of the new material,and once some machines experienced problems, sensitivity to the newmaterial was increased and operators on machines that had not seenproblems now reported them.

In order to achieve an accurate and unbiased assessment of the totalfinancial impact of the material change, the PIPE database was searchedfor 28 different categories of machine interruption events known to becaused by the raw material change. The number of events and downtimecaused by each event was tallied for each machine on a day-to-day basisfor a baseline period before the conversion and for a period after theconversion. All machines were then placed on a normalized time scalewhere the conversion day was marked by 0. This method allowed theassessment of the average relative impact of the raw material change. Afinancial-based formula calculated the combined cost of machine stopsand downtime minutes.

Results indicated that for a three-month period after the raw materialconversion, there was a significant increase in stops and downtimeminutes due to the new material, validating what was experienced at theproduction facilities. This increase may have been enough to warrant achange to the previous material. A key result from the analysis wasthat, after the three-month period, the number of stops and the amountof delay was not statistically different than the average pre-conversionbaseline. This indicated that after a relatively troublesome conversiontime, the new raw material may be used at significant cost savings.

The above example required after-the-fact mining of PIPE data by a humanagent. The mining of the PIPE data to identify the financial costs of araw material change may, in an extension of the above example, bemodified to be generated automatically. For example, a central servermay be programmed to compile production data from various machines orplants that implement a change in a raw material or other aspects of therecipe for a product, or that make a change in equipment, processconditions, and the like. For meaningful analysis of the effect of thechange in productivity, the rolled-up data from various sites must benormalized to provide a cumulative time series having a common timelinebased on the day that the change was made, which may correspond todifferent actual dates for the various sites. Thus, the time-series datafor each machine or plant may need to be shifted so that the cumulative,averaged, or weighted combination of the data yields a time series witha common origin (day zero) corresponding to the change (or to anarbitrary period of time before of after the change).

In one embodiment, PIPE data from each plant includes information onchanges and dates of the change for any change in product recipe,equipment, crew size, etc., so that similar changes at other facilitiesmay be compared based on a meaningful normalized timeline that mayrequire shifting the actual dates for many or all raw data series toprovide normalized productivity results for a particular change. In arelated embodiment, a user such as an accountant or supervisor may beprovided with a menu on a web-based display or other software system,allowing selection of one of several process or raw material changesthat have been implemented at one or more facilities, whereupon anormalized time series is displayed showing before and after data fromall involved facilities (one data point per facility per reportingperiod is displayed) or of combined data (one data point per reportingperiod, averaged or summed or otherwise combined from the results fromvarious facilities for the reporting period), wherein the data typedisplayed may be selected from a list of available productivity andfinancial parameters. The resulting data sets displayed may be smoothed,statistically analyzed, transformed, displayed on a web page,incorporated as a graph into a report, and so forth. In this manner, theimpact of various changes at various facilities executed at varioustimes may be compared in a meaningful way, allowing combined before andafter results to be displayed and further analyzed, as desired, toassess the financial impact of a change (e.g., its impact on anyselected productivity measure, including net profit, operating cost,etc.).

EXAMPLE 2

In the production of an article including a web, the web had beenslitted to a narrow width suitable for the article. The narrow roll ofthe slitted web used as a raw material was produced in another processby slitting a wider roll of the web material. Each slitted roll of theweb material was tracked with bar code information that identified theroll, its date and place of manufacturer, and its position on theslitter. The slitter had a plurality of slitter blades and formed aplurality of narrow slitted sections of the web numbered from 1 to N,where N is the number of slit sections and N−1 is the number of slitterblades used. The outer positions of the web were sections 1 and N, whilethe central position of the web was position N/2 (for even N). The rollsof raw material were used on a machine for producing an article. Themachine was equipped with a PIPE system to track process events such aswaste and delay at various locations in the machine. Over a period oftime, production data was obtained for articles produced with the websfrom all slitter positions, and each product was associated with a lotnumber that may be used to identify which slitter position theassociated web came from.

PIPE reporting systems were then used to display cumulative delay duringthe production period, filtered to only consider delay associated withweb handling problems related to the slit web (e.g., a subset of thelogged delay codes were used that were known to be linked to webhandling problems). The results were further segregated according tosplitter position for the web, which was possible because raw materialinformation (including slitter position of the web) had been recordedand was accessible via the PIPE database for the production period.Delay results were displayed as a bar graph, with N bars representingcumulative web-related waste for each of the N slitter positions. Theouter bars, corresponding to slitter positions 1 and N, the outsidepositions, showed the highest delay. Since the web at the outerpositions is less restrained than the web in the inner positions duringa slitting operation, it is believed to be more subject to flutter,drift, tearing, or other problems, so one may understand that slittingmay be more problematic at the outer positions. Problems in slitting forthe outermost positions appeared to be clearly reflected in the reporteddelay rates grouped by slitter position. This method allowed the impactof the problem to be quantified and its effect on financial returns tobe determined. Decisions regarding process improvements may then beintelligently pursued and evaluated on a sound financial basis, such asconsideration of adding additional restraint to the outer edges of theweb such as slitting an thin trim section to be discarded or findingalternative uses for outer portions of the slit web or adjustingproduction conditions in making a product that employs web slit at theouter positions of a slitter to compensate for expected web handlingproblems.

In a related embodiment, a quality control system (including oneassociated with a PIPE system) in the web slitting facility may detectand log possible web quality problems through the use of machine visionduring slitting or through other sensors, and this information as afunction of the position inside each roll may be fed to the articleproduction line (e.g., into a STORM system) to permit adaptive responseof the production line to deal with anticipated quality problems atspecific locations in the roll of the slitted web. In this manner, wasteand delay may be reduced by communication between a machine producing araw material and the machine using the raw material, particularly with afeed-forward system based on measured attributes of the raw materialsbeing used.

EXAMPLE 3

This example deals with analysis of actual PIPE data obtained with aproprietary PIPE-based manufacturing system in which manufacturingevents from a consumer products machine were recorded over time. Themachine processed a variety of raw materials, including webs of materialprovided in roll form. To maintain a continuous web being fed into themachine, web splices were routinely performed, particularly between thetail end of one roll and the leading edge of the next roll.

During the review of PIPE summary data of machine stops and delay on theconsumer products machine, a particular machine section was identifiedas contributing an excessively large percent of the total number ofdaily machine stops. A number of possible reasons for the increasedsection stops were hypothesized, some of which were connected to variousother events that occurred on the machine. In particular, various rawmaterial splice events were suspected as possible causes for theincreased stops in the particular machine section. In order to testthese hypotheses, PIPE data were retrieved for every machine stop thatoriginated in the particular machine section over a number of months.From these data, the exact time of each stop was obtained. These eventtimes were tested for correlation with other machine events such asmachine start-up, raw material splice on, etc. (The occurrence times forthese other events were also obtained from the PIPE and STORMdatabases.) Analysis showed that the machine section stops were highlycorrelated with the splice-on event of one particular raw material.

FIG. 14 shows the results of this analysis. The normalized probabilityof the particular machine section causing a stop is plotted against thetemporal distance from the raw material splice-on event. A markedincrease in the probability of a stop around this event is evident. Inaddition to showing a strong relationship between the two events, adistinct bi-modal distribution in the probability of a stop is apparent,suggesting that at least two different failure mechanisms wereresponsible for the section stops. The relative frequency of each peakof the distribution allowed proper prioritization of recourses tocorrect the problem.

In many modern converting lines, such as those that manufacture personalcare articles, different events, such as new rolls of raw materialsplicing on, may occur every few minutes. It is highly improbable for anoperator to be able to discern, on a long-term and statisticallysignificant basis, between the many possible cause-and-effectrelationships that may cause machine stops. The present exampleillustrates the potential of mining PIPE data for correlations toidentify previously unrecognized problems or opportunities.

EXAMPLE 4

Examples of financial reports automatically obtainable by a PIPE systemfor a hypothetical manufacturing operation are shown in FIG. 15 to FIG.18. FIG. 15 shows the yield in a plant over a ten-week period, showingweekly averages, and a moving three-week average as well as the averagefrom the previous quarter. The report includes information specifyingthe machine and reported Waste Opportunity Cost (labeled as yieldopportunity costs). FIG. 16 shows a related report from the same mill,but reporting uptime results instead of yield. The Downtime OpportunityCost is also reported. The total opportunity cost is the sum of theDowntime Opportunity Cost and Waste Opportunity Cost.

In another embodiment, a user generates curves in a financial reportthat represent different machine uptimes for a process having multipleprocess steps. The actual data for individual processes may then beplotted to give a visual representation of the performance of theindividual processes. From this one plot, an analyst may view processperformance and analyze how duration and frequency play a role inproducing that result. In addition, tables with data for varyingpercentages of machine uptime may also be generated for analysis.

In another example, a normalized process/section uptime numberfacilitates comparison not only to other process sections but also to amachine uptime number. The machine uptime number is generated byfactoring in the number of process sections in the machine indetermining process uptime. By plotting process results on a graph, ananalyst may view overall performance, variability in performance,contributing factors to performance, and frequency or duration. Theresults may be grouped into levels of performance that potentially maybe related to the types of corrective action. For example, differentcorrective actions may be employed if the process uptime is greater thanseventy percent, less than seventy percent but greater than fiftypercent, or less than fifty percent. Those skilled in the art will notethat the percentage thresholds are exemplary and will vary in differentembodiments.

FIG. 17 shows opportunity costs for a single delay event (spray nozzlefailure, listed as code 114) on a single machine, showing weekly resultsover ten weeks, and a three-week moving average. A projected annualopportunity cost for this event is reported to allow administrators andoperators to understand the financial impact of the event.

FIG. 18 shows a bar chart in which the top six most costly causes ofopportunity loss are reported for a specified time period on aparticular machine.

The reports may be generated in response to a user request or may bespecified for automatic display and updating to permit processadministrators to review the information periodically.

EXAMPLE 5

A prophetic example is described illustrating how a PIPE database may bemined to identify the importance of previously unrecognized variablesthat may require further monitoring to improve a process or avoidproduction problems. Analysis of production event from a single machineor single production facility frequently may be inadequate to identifycauses of quality problems, particularly when the cause is associatedwith factors that are not being included in the database. For example, aproduction problem at a certain time in a cosmetic production line maynot have any apparent link to measured raw material properties, linespeed, process temperature, and other measured parameters. To explorethe possibility of environmental or other factors being associated withthe production problem, PIPE event data from other machines in the sameproduction facility may then be analyzed to see if there were relatedproduction problems at the same time. If so, a hypothesis may be offeredthat an environmental or system factor may be at fault, such as processwater properties (temperature, pH, dissolved solids, pressure, etc.),humidity, air temperature, dust or other contaminants in the air, a dropor surge in the voltage of electricity provided to the facility, anoutbreak of mold in the facility, and the like. Alternatively or inaddition, archived data from other sources may be examined, such as alog of water quality measurements, weather information, process waterpressure measurements, and the like. The other source of data may beexternal to the production facility, such as weather data recorded by ameteorological station.

The PIPE system may automatically search other databases to identifypossible factors that could account for quality problems, or may informa human supervisor of the existence of common factors that may beassociated with related production problems on multiple machines, andrequest further action to identify suspected factors. Once factors areidentified as having relevance to a production problem, these variablesmay be routinely monitored as part of the process control approach forthe machine and the variables may be incorporated into the PIPEdatabase.

Likewise, production problems with no apparent cause may be used toinfer that other factors are playing a role. Steps may then be taken tofurther identify the factors and add them to the list of variables beingmonitored in production. These steps may include accessing otherdatabases to search for correlations, or using human input or expertsystems to propose hypothesis that may then be tested, typically viaexpanded measurement of raw material properties, process performance,machine conditions, and so forth, to temporarily obtain additional datauntil likely causes are identified.

EXAMPLE 6

One prophetic example of a graphical interface for access toproductivity reports is described. For a corporation with multipleproduction facilities, the PIPE databases and associated databases areaccessed and filtered to provide a wide array of customized reportsbased on user input via a graphical interface. At one level, theinterface shows an electronic map depicting icons for the units such asthe corporation as a whole and the various sectors and the productionfacilities associated with the sectors. The interface may be providedvia a web browser using tools such as JAVASCRIPT brand computerprograms, ACTIVEX controls, XML, DHTML, FLASH brand computer software(Macromedia Corp.), and the like, or may be provided via other means,such as a graphical display in a Human-Machine Interface system such asWONDERWARE brand manufacturing and process control operator-machineinterface software, a VISIO brand drawing and diagramming softwareapplication (Microsoft Corp.), an electronic map of Business EnterpriseMapping (Scottsdale, Ariz.), or other customized software. Selectionsmade via controls on the screen, including selections offered byclicking on icons, may generate productivity reports for a user-definedperiod of time, product category (including all categories), or soforth. For example, an officer of the corporation may wish to reviewoverall financial information for all production facilities makinghealth-care products for the current quarter. Upon opening a PIPE reportscreen via a web browser connected to the corporate Intranet, a screenis provided offering various controls to select the report parameters,and also displaying icons for various corporate entities, such as theoverall corporation, its sectors or divisions, and the plants associatedwith each sector. In one embodiment, the screen is provided in a mannerthat shows relationships between the entities (such as an organizationchart with lines depicting relationships).

A quarter-to-date radio button on the screen may be selected, a checkboxfor “waste” may be checked, and a health care category may be selectedin a drop down box also on the screen. At this point, the icons forcorporate entities on the screen may change in size, color, textualannotation, and/or associated controls (e.g., offered hypertext links,drop-down box items or pop-up menu selections) to convey informationabout the status of the entities. For example, icons representingsectors or mills that had acceptable levels of waste may appear green,and those with waste above a threshold value (with waste measurementbased on wasted product numbers or percentage of waste or a financialmeasure of the impact of waste, for example) may appear red, with thecolor saturation being a function of the magnitude of the waste level.Statistics may appear in a box or column associated with each icon toreport desired parameters, and charts for waste incidents or otherproductivity measures may be offered for any plant or sector by clickingon the icon and making a selection from a pop-up menu. Double-clickingon an icon for a mill (or other suitable actions) may expand the displayto show icons or other graphics representing processes or machines in amill, optionally with display of a new window to show mill-relatedgraphics. A new window, for example, may show a graphical depiction ofprocesses used to manufacture the products made at the mill, with theprocesses being depicted as flow charts or as interrelated machinesdepicted with icons showing the relationship between the components(hardware, raw materials, final products, etc.). Productivity resultsmay be displayed for each process for a given product, allowing theviewer to rapidly identify which parts of the process or of a machinewere causing the greatest waste or delay. For example, a graphicaldepiction of a papermaking machine might show a square representingstock prep, connected by a line to a box representing a forming section,connected by another line to an icon representing a press section,connected by another line to an icon representing a drying section andfinally connected by another line to a box representing a winder. PIPEinformation may be dynamically linked to the display such that the partsof the process were displayed in a color indicating the level of delaycaused by events associated with the respective parts. Thus, for auser-defined reporting period such as quarter-to-date, the dryingsection may be colored in bright red to indicate that it has contributedsubstantially more delay than other components. Clicking on the dryersection may then provide more detailed information about the nature ofthe events, their frequency, and so forth.

In general, an interactive electronic map with multiple levels of detailmay be provided to convey both text and graphical indications of themanufacturing performance of various entities in the corporation, allthe way down to specific components of an individual machine in aparticular plant. The interactive displays may be linked to dynamicallyprovide more detailed information via spreadsheets or other reportingmeans. Waste, productivity, and quality issues may be viewed at aroll-up level for sectors, product categories, market categories,specific intervals of times, and so forth, with expanding displaysproviding to interactively allow the user to see how the results for oneentity are distributed between the parts that make up the entity (e.g.,multiple plants for a sector or various machines within a plant or theparts of a single machine).

Exemplary Operating Environments

The invention provides an intelligent manufacturing system including aprocess for converting raw materials to a product, a process controlsystem including one or more sensors capable of generating an alarm inresponse to an event that results in one of waste, machine delay, ordecrease product quality, a data logger associated with the processcontrol system for obtaining event parameters associated with the event,a database on a server for recording event parameters obtained by thedata logger, and a reporting system cooperatively associated with thedatabase for reporting productivity parameters regarding the processderived at least in part from the event parameters.

In one embodiment, one or more computing devices implement the inventionas illustrated and described herein. For example, the computing devicesmay include a personal computer (PC), a mainframe, a personal digitalassistant (PDA), or a combination of various computing devices or thelike. The computing devices may communicate with each other and/or withother computing devices via one or more networks such as an intranet orthe Internet.

In addition, the computing devices may have access to one or morecomputer-readable media storing data such as computer-readableinstructions and data structures. The computing devices execute thestored computer-readable instructions to perform the tasks embodied bythe computer-readable instructions. The computer-readable media storedata in a data signal (e.g., a carrier wave). Those skilled in the artwill note that the data signal has one or more of its characteristicsset or changed in such a manner as to encode information in the datasignal. As used herein, the terms “computer-readable medium” and“computer-readable media” encompass data signals. Further, the terms“computer-readable medium” and “computer-readable media” encompass asmart tag, a memory device, or any other device storing data and locatedproximal to a material such as a raw material or an intermediateproduct.

One or more computer-readable media have computer-executable componentsincluding a control module and a database module. The control modulecollects, during a first process, event data relating to a material. Thedatabase module stores the event data collected by the control module asa data record. An identifier within the database module is associatedwith the data record. The data record is accessible via its associatedidentifier so that the collected event data in the database module isobtainable during a second process occurring subsequent to the firstprocess.

One or more computer-readable media store a data structure representingan identifier for a material in an event-based manufacturing system. Thedata structure includes a first field storing a vendor code representinga vendor of the material. The data structure also includes a secondfield storing a batch code assigned by the vendor representing a batchof the material.

In an event-based manufacturing system, one or more computer-readablemedia for use in conjunction with a second process occurring after afirst process, store a data structure representing event data for amaterial. The data structure includes one or more fields storing datadescribing characteristics of the material. The data structure ispopulated during the first process and is accessible during the secondprocess.

When introducing elements of the present invention or the embodiment(s)thereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

In view of the above, it will be seen that the several objects of theinvention are achieved and other advantageous results attained.

As various changes may be made in the above systems and methods withoutdeparting from the scope of the invention, it is intended that allmatter contained in the above description and shown in the accompanyingdrawings shall be interpreted as illustrative and not in a limitingsense.

1. A system for storing, during a process, data associated with a material, the system comprising: a control system for collecting, during a first process, event data relating to a material, the event data comprising an event code and a value pertaining to an attribute or physical property of the material affected by the event; and a memory device for storing the collected event data as a data record, wherein an identifier within the memory device is associated with the data record, the data record being accessible via its associated identifier so that the collected event data in the memory device is obtainable during a second process occurring subsequent to the first process, the second process adapted to be modified responsive to the event data.
 2. The system of claim 1, further comprising a label including the identifier, and wherein the label is located proximal to the material.
 3. The system of claim 1, wherein the memory device is located proximal to the material.
 4. The system of claim 1, wherein the memory device is one of a plurality of separate memory devices storing the collected event data, and wherein the identifier is associated with collected event data in the plurality of separate memory devices.
 5. The system of claim 1, wherein the identifier comprises one or more of the following: a smart tag, an ultra-wide band identification device, a bar code, and a hyperlink.
 6. The system of claim 1, wherein the identifier comprises a plurality of smart tags embedded throughout the material.
 7. The system of claim 1, wherein the memory device comprises one or more of the following: a smart tag, an ultra-wide band identification device, and a database.
 8. The system of claim 1, wherein the identifier comprises: a vendor code identifying a vendor of the material; and a batch code identifying a batch of the material.
 9. The system of claim 8, wherein a manufacturer supplies the vendor code.
 10. The system of claim 8, wherein a vendor supplies the batch code.
 11. The system of claim 1, wherein the material comprises an intermediate product.
 12. The system of claim 1, wherein the material comprises a raw material.
 13. The system of claim 1, wherein the control system collects event data during manufacturing of a product using the material.
 14. The system of claim 1, wherein the event data comprises one or more of the following: a production history, a certificate of analysis, a table of properties for the raw material, a quality attribute, a list of materials used in producing the raw material, target operating parameters, actual operating parameters, vendor identification, a production date, consumed raw material information, partially consumed raw material information, rejected raw material information, and a product counter.
 15. The system of claim 14, wherein the quality attribute comprises one or more of the following: a quality statistic, data from a process control system during production, and a time-series of raw data.
 16. A method for storing data associated with a material, the method comprising: collecting, during a first process, event data relating to a material; and storing the collected event data as a data record, wherein the event data comprises information indicating the location within the material where a quality defect may occur, wherein an identifier is associated with the data record, the data record being accessible via its associated identifier so that the collected event data is obtainable during a second process occurring subsequent to the first process, the second process adapted to be modified responsive to the event data to reduce the impact on the process of a quality defect in the material.
 17. The method of claim 16, further comprising: receiving a request from a control system of the second process for the stored event data; and forwarding the stored event data to the control system in response to the received request.
 18. The method of claim 16, wherein a label includes the identifier, and wherein the label is located proximal to the material.
 19. The method of claim 16, wherein the data record is located proximal to the material.
 20. The method of claim 16, wherein the storing comprises storing the collected event data as a data record in a plurality of separate memory devices, and wherein the identifier is associated with the stored event data in the plurality of separate memory devices.
 21. The method of claim 16, wherein the storing comprises storing the collected event data as a data record in a memory device, wherein the memory device comprises one or more of the following: a smart tag, an ultra-wide band identification device, and a database.
 22. The method of claim 16, wherein the identifier comprises: a vendor code identifying a vendor of the material; and a batch code identifying a batch of the material.
 23. The method of claim 16, wherein the material comprises an intermediate product.
 24. The method of claim 16, wherein the identifier comprises a smart tag, and wherein the collecting comprises embedding the smart tag in packaging associated with the material.
 25. The method of claim 16, wherein the identifier comprises a smart tag, wherein the material comprises a raw material, and wherein the collecting comprises embedding the smart tag in an intermediate material during production of the raw material.
 26. The method of claim 16, wherein collecting comprises collecting an electronic certificate of analysis and/or purchase order information for a material during the first process.
 27. The method of claim 16, further comprising editing the stored event data.
 28. The method of claim 27, wherein the editing comprises adding or viewing at least one comment associated with the stored event data.
 29. The method of claim 16, further comprising sending an alert to an operator in response to the collecting.
 30. The method of claim 16, wherein one or more computer-readable media have computer-executable instructions for performing the method of claim
 16. 31. The method of claim 16, further comprising mining the stored event data during the second process to identify a modification to the material and/or the first process, wherein implementation of the modification results in an improvement to the material and/or the first process.
 32. The method of claim 16, further comprising analyzing the stored event data to optimally schedule maintenance related to the first process.
 33. One or more computer-readable media having computer-executable components comprising: a control module collecting, during a first process, event data relating to a material; and a database module storing the event data collected by the control module as a data record, wherein an identifier within the database module is associated with the data record, the data record being accessible via its associated identifier so that the collected event data in the database module is obtainable during a second process occurring subsequent to the first process, wherein the event data is used to modify an operating condition of the second process.
 34. The computer-readable media of claim 33, wherein the database module receives a request from a control system of the second process for the stored event data and forwards the stored event data to the control system in response to the received request.
 35. The computer-readable media of claim 33 further comprising a label including the identifier, wherein the label is located proximal to the material.
 36. The computer-readable media of claim 33, wherein the database module is located proximal to the material.
 37. The computer-readable media of claim 33, wherein the database module comprises a plurality of separate memory devices storing the data record, and wherein the identifier is associated with the stored event data in the plurality of separate memory devices.
 38. The computer-readable media of claim 33, wherein the database module comprises a memory device, the memory device comprising one or more of the following: a smart tag, an ultra-wide band identification device, and a database.
 39. The computer-readable medium of claim 33, further compressing a work orders module to generate work orders based on the event data in the database module.
 40. The computer-readable medium of claim 33 further comprising a maintenance module to schedule maintenance in response to the event data stored in the database module. 